{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a54aed1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b717ceb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('../data/us_6.0.csv')\n",
    "dfBeforeOmicron = pd.read_csv('../data/us_6.2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4c4c2926",
   "metadata": {},
   "outputs": [],
   "source": [
    "dates = pd.to_datetime(df['date'])\n",
    "cols = list(df)[1:9]\n",
    "df = df[cols].astype(float)\n",
    "# df.shape\n",
    "\n",
    "dates_before = pd.to_datetime(dfBeforeOmicron['date'])\n",
    "cols_before = list(dfBeforeOmicron)[1:9]\n",
    "dfBeforeOmicron = dfBeforeOmicron[cols_before].astype(float)\n",
    "# dates_before"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a5221c8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def df_to_X_y(df, window_size=7):\n",
    "  df_as_np = df.to_numpy()\n",
    "  X = []\n",
    "  y = []\n",
    "  for i in range(len(df_as_np)-window_size):\n",
    "    row = [r for r in df_as_np[i:i+window_size]]\n",
    "    X.append(row)\n",
    "    label = df_as_np[i+window_size][0]\n",
    "    y.append(label)\n",
    "  return np.array(X), np.array(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "adffc253",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import mean_squared_error as mse # mse\n",
    "from sklearn.metrics import mean_absolute_error as mae # mae\n",
    "from sklearn.metrics import mean_absolute_percentage_error as mape # mape\n",
    "def plot_predictions1(model, X, y, start=0, end=100):\n",
    "  predictions = model.predict(X)\n",
    "  \n",
    "#   plt.plot(df['Predictions'][start:end])\n",
    "#   plt.plot(df['Actuals'][start:end])\n",
    "  return predictions, mse(y, predictions), mae(y, predictions), mape(y, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "29d7d55b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "# 先转换数据，后拆分\n",
    "scaler = StandardScaler()\n",
    "scaler = scaler.fit(dfBeforeOmicron)\n",
    "df_scaled = scaler.transform(df)\n",
    "# df_scaled\n",
    "\n",
    "# scalerBeforeOmicron = StandardScaler()\n",
    "# scalerBeforeOmicron = scaler.fit(dfBeforeOmicron)\n",
    "dfBeforeOmicron_scaled = scaler.transform(dfBeforeOmicron)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1951e89e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_scaled = pd.DataFrame(df_scaled, columns=['total_cases','new_cases_smoothed','total_deaths','new_deaths_smoothed','stringency_index','neg','neu','pos'])\n",
    "x2, y2 = df_to_X_y(df_scaled, window_size=7)\n",
    "# x2\n",
    "\n",
    "dfBeforeOmicron_scaled = pd.DataFrame(dfBeforeOmicron_scaled, columns=['total_cases','new_cases_smoothed','total_deaths','new_deaths_smoothed','stringency_index','neg','neu','pos'])\n",
    "x2_before, y2_before = df_to_X_y(dfBeforeOmicron_scaled, window_size=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5d73d604",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((30, 7, 8), (30,), (30, 7, 8), (30,))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X2_train, y2_train = x2[:260], y2[:260]\n",
    "X2_val, y2_val = x2[260:290], y2[260:290]\n",
    "X2_test, y2_test = x2[267:], y2[267:]\n",
    "# X2_train.shape, y2_train.shape, X2_val.shape, y2_val.shape, X2_test.shape, y2_test.shape\n",
    "\n",
    "\n",
    "X2_before_train, y2_before_train = x2_before[:260], y2_before[:260]\n",
    "X2_before_val, y2_before_val = x2_before[260:290], y2_before[260:290]\n",
    "X2_before_test, y2_before_test = x2_before[269:], y2_before[269:]\n",
    "\n",
    "X2_test.shape, y2_test.shape, X2_before_test.shape, y2_before_test.shape\n",
    "\n",
    "# y2_test,y2_before_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7c45d1e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import *\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint\n",
    "from tensorflow.keras.losses import MeanSquaredError\n",
    "from tensorflow.keras.metrics import RootMeanSquaredError\n",
    "from tensorflow.keras.optimizers import Adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ad839675",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "9/9 [==============================] - 1s 16ms/step - loss: 372.9131 - root_mean_squared_error: 19.3110 - val_loss: 523.2385 - val_root_mean_squared_error: 22.8744\n",
      "Epoch 2/50\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 360.7753 - root_mean_squared_error: 18.9941 - val_loss: 509.0128 - val_root_mean_squared_error: 22.5613\n",
      "Epoch 3/50\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 351.4274 - root_mean_squared_error: 18.7464 - val_loss: 495.3074 - val_root_mean_squared_error: 22.2555\n",
      "Epoch 4/50\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 341.8234 - root_mean_squared_error: 18.4885 - val_loss: 482.0475 - val_root_mean_squared_error: 21.9556\n",
      "Epoch 5/50\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 334.2926 - root_mean_squared_error: 18.2837 - val_loss: 468.9910 - val_root_mean_squared_error: 21.6562\n",
      "Epoch 6/50\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 322.7536 - root_mean_squared_error: 17.9653 - val_loss: 456.1947 - val_root_mean_squared_error: 21.3587\n",
      "Epoch 7/50\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 315.9350 - root_mean_squared_error: 17.7746 - val_loss: 443.5307 - val_root_mean_squared_error: 21.0602\n",
      "Epoch 8/50\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 307.1246 - root_mean_squared_error: 17.5250 - val_loss: 430.9913 - val_root_mean_squared_error: 20.7603\n",
      "Epoch 9/50\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 297.5155 - root_mean_squared_error: 17.2486 - val_loss: 418.7697 - val_root_mean_squared_error: 20.4639\n",
      "Epoch 10/50\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 288.9264 - root_mean_squared_error: 16.9978 - val_loss: 406.4666 - val_root_mean_squared_error: 20.1610\n",
      "Epoch 11/50\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 279.8203 - root_mean_squared_error: 16.7278 - val_loss: 394.2329 - val_root_mean_squared_error: 19.8553\n",
      "Epoch 12/50\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 272.4828 - root_mean_squared_error: 16.5071 - val_loss: 382.0722 - val_root_mean_squared_error: 19.5467\n",
      "Epoch 13/50\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 263.6530 - root_mean_squared_error: 16.2374 - val_loss: 369.8853 - val_root_mean_squared_error: 19.2324\n",
      "Epoch 14/50\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 255.1113 - root_mean_squared_error: 15.9722 - val_loss: 357.5799 - val_root_mean_squared_error: 18.9098\n",
      "Epoch 15/50\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 247.0141 - root_mean_squared_error: 15.7167 - val_loss: 344.9501 - val_root_mean_squared_error: 18.5728\n",
      "Epoch 16/50\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 236.9591 - root_mean_squared_error: 15.3935 - val_loss: 332.2504 - val_root_mean_squared_error: 18.2277\n",
      "Epoch 17/50\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 228.1757 - root_mean_squared_error: 15.1055 - val_loss: 319.4008 - val_root_mean_squared_error: 17.8718\n",
      "Epoch 18/50\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 220.0846 - root_mean_squared_error: 14.8352 - val_loss: 306.1399 - val_root_mean_squared_error: 17.4969\n",
      "Epoch 19/50\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 210.1926 - root_mean_squared_error: 14.4980 - val_loss: 292.4555 - val_root_mean_squared_error: 17.1013\n",
      "Epoch 20/50\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 200.7388 - root_mean_squared_error: 14.1682 - val_loss: 278.6635 - val_root_mean_squared_error: 16.6932\n",
      "Epoch 21/50\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 190.5472 - root_mean_squared_error: 13.8039 - val_loss: 264.5681 - val_root_mean_squared_error: 16.2655\n",
      "Epoch 22/50\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 179.6635 - root_mean_squared_error: 13.4039 - val_loss: 250.3234 - val_root_mean_squared_error: 15.8216\n",
      "Epoch 23/50\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 169.2968 - root_mean_squared_error: 13.0114 - val_loss: 235.1950 - val_root_mean_squared_error: 15.3361\n",
      "Epoch 24/50\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 156.7623 - root_mean_squared_error: 12.5205 - val_loss: 218.7923 - val_root_mean_squared_error: 14.7916\n",
      "Epoch 25/50\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 143.6239 - root_mean_squared_error: 11.9843 - val_loss: 200.9632 - val_root_mean_squared_error: 14.1761\n",
      "Epoch 26/50\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 130.4783 - root_mean_squared_error: 11.4227 - val_loss: 181.5782 - val_root_mean_squared_error: 13.4751\n",
      "Epoch 27/50\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 115.8214 - root_mean_squared_error: 10.7620 - val_loss: 161.1620 - val_root_mean_squared_error: 12.6950\n",
      "Epoch 28/50\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 101.4957 - root_mean_squared_error: 10.0745 - val_loss: 139.5408 - val_root_mean_squared_error: 11.8127\n",
      "Epoch 29/50\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 85.0946 - root_mean_squared_error: 9.2247 - val_loss: 117.6526 - val_root_mean_squared_error: 10.8468\n",
      "Epoch 30/50\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 71.8773 - root_mean_squared_error: 8.4780 - val_loss: 97.5190 - val_root_mean_squared_error: 9.8752\n",
      "Epoch 31/50\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 60.3974 - root_mean_squared_error: 7.7716 - val_loss: 82.2046 - val_root_mean_squared_error: 9.0667\n",
      "Epoch 32/50\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 50.0878 - root_mean_squared_error: 7.0773 - val_loss: 68.7461 - val_root_mean_squared_error: 8.2913\n",
      "Epoch 33/50\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 41.4930 - root_mean_squared_error: 6.4415 - val_loss: 56.5220 - val_root_mean_squared_error: 7.5181\n",
      "Epoch 34/50\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 33.6214 - root_mean_squared_error: 5.7984 - val_loss: 45.3974 - val_root_mean_squared_error: 6.7378\n",
      "Epoch 35/50\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 26.5342 - root_mean_squared_error: 5.1511 - val_loss: 34.9492 - val_root_mean_squared_error: 5.9118\n",
      "Epoch 36/50\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 18.8894 - root_mean_squared_error: 4.3462 - val_loss: 23.7668 - val_root_mean_squared_error: 4.8751\n",
      "Epoch 37/50\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 12.4810 - root_mean_squared_error: 3.5329 - val_loss: 14.1722 - val_root_mean_squared_error: 3.7646\n",
      "Epoch 38/50\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 7.4999 - root_mean_squared_error: 2.7386 - val_loss: 6.9408 - val_root_mean_squared_error: 2.6345\n",
      "Epoch 39/50\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 3.6526 - root_mean_squared_error: 1.9112 - val_loss: 2.4849 - val_root_mean_squared_error: 1.5764\n",
      "Epoch 40/50\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 1.6310 - root_mean_squared_error: 1.2771 - val_loss: 0.5301 - val_root_mean_squared_error: 0.7281\n",
      "Epoch 41/50\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.9250 - root_mean_squared_error: 0.9618 - val_loss: 0.0799 - val_root_mean_squared_error: 0.2826\n",
      "Epoch 42/50\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 1.0217 - root_mean_squared_error: 1.0108 - val_loss: 0.0472 - val_root_mean_squared_error: 0.2173\n",
      "Epoch 43/50\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.7978 - root_mean_squared_error: 0.8932 - val_loss: 0.0502 - val_root_mean_squared_error: 0.2241\n",
      "Epoch 44/50\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.9605 - root_mean_squared_error: 0.9801 - val_loss: 0.0699 - val_root_mean_squared_error: 0.2645\n",
      "Epoch 45/50\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.7265 - root_mean_squared_error: 0.8523 - val_loss: 0.1215 - val_root_mean_squared_error: 0.3486\n",
      "Epoch 46/50\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.8607 - root_mean_squared_error: 0.9278 - val_loss: 0.1700 - val_root_mean_squared_error: 0.4124\n",
      "Epoch 47/50\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.7344 - root_mean_squared_error: 0.8570 - val_loss: 0.1691 - val_root_mean_squared_error: 0.4112\n",
      "Epoch 48/50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 0s 3ms/step - loss: 0.4998 - root_mean_squared_error: 0.7070 - val_loss: 0.1674 - val_root_mean_squared_error: 0.4091\n",
      "Epoch 49/50\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.8658 - root_mean_squared_error: 0.9305 - val_loss: 0.1668 - val_root_mean_squared_error: 0.4084\n",
      "Epoch 50/50\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.5813 - root_mean_squared_error: 0.7625 - val_loss: 0.1749 - val_root_mean_squared_error: 0.4182\n"
     ]
    }
   ],
   "source": [
    "# LSTM\n",
    "model_lstm = Sequential()\n",
    "model_lstm.add(InputLayer((7, 8)))\n",
    "model_lstm.add(Conv1D(32, activation='relu', input_shape=(X2_train.shape[1], X2_train.shape[2]), kernel_size=2))\n",
    "model_lstm.add(Dropout(0.01))\n",
    "model_lstm.add(GRU(16, activation='relu', input_shape=(X2_train.shape[1], X2_train.shape[2])))\n",
    "model_lstm.add(Dropout(0.01))\n",
    "# model_lstm.add(Attention(use_scale=False))\n",
    "# model_lstm.add(Dropout(0.1))\n",
    "# model_lstm.add(Dense(8, 'relu'))\n",
    "model_lstm.add(Dense(1, 'linear'))\n",
    "\n",
    "# model_Multivariate.build(input_shape=(None, 64, 8, 1))\n",
    "# model_Multivariate.summary()\n",
    "\n",
    "# cp_lstm = ModelCheckpoint('model_lstm/', save_best_only=True)\n",
    "model_lstm.compile(loss=MeanSquaredError(), optimizer=Adam(learning_rate=0.0001), metrics=[RootMeanSquaredError()])\n",
    "history = model_lstm.fit(X2_train, y2_train, batch_size=32, validation_data=(X2_val, y2_val), epochs=50)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1533,
   "id": "1962e2a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/80\n",
      "9/9 [==============================] - 0s 13ms/step - loss: 27.7340 - root_mean_squared_error: 5.2663 - val_loss: 22.6421 - val_root_mean_squared_error: 4.7584\n",
      "Epoch 2/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 22.1868 - root_mean_squared_error: 4.7103 - val_loss: 19.9365 - val_root_mean_squared_error: 4.4650\n",
      "Epoch 3/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 17.3783 - root_mean_squared_error: 4.1687 - val_loss: 17.3862 - val_root_mean_squared_error: 4.1697\n",
      "Epoch 4/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 13.3000 - root_mean_squared_error: 3.6469 - val_loss: 14.9470 - val_root_mean_squared_error: 3.8661\n",
      "Epoch 5/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 9.8331 - root_mean_squared_error: 3.1358 - val_loss: 12.7082 - val_root_mean_squared_error: 3.5649\n",
      "Epoch 6/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 7.0380 - root_mean_squared_error: 2.6529 - val_loss: 10.6993 - val_root_mean_squared_error: 3.2710\n",
      "Epoch 7/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 4.8696 - root_mean_squared_error: 2.2067 - val_loss: 8.9057 - val_root_mean_squared_error: 2.9842\n",
      "Epoch 8/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 3.2663 - root_mean_squared_error: 1.8073 - val_loss: 7.4044 - val_root_mean_squared_error: 2.7211\n",
      "Epoch 9/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 2.1722 - root_mean_squared_error: 1.4738 - val_loss: 6.1340 - val_root_mean_squared_error: 2.4767\n",
      "Epoch 10/80\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 1.4909 - root_mean_squared_error: 1.2210 - val_loss: 5.1117 - val_root_mean_squared_error: 2.2609\n",
      "Epoch 11/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 1.1451 - root_mean_squared_error: 1.0701 - val_loss: 4.3414 - val_root_mean_squared_error: 2.0836\n",
      "Epoch 12/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.9782 - root_mean_squared_error: 0.9890 - val_loss: 3.8111 - val_root_mean_squared_error: 1.9522\n",
      "Epoch 13/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.9105 - root_mean_squared_error: 0.9542 - val_loss: 3.4549 - val_root_mean_squared_error: 1.8587\n",
      "Epoch 14/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.8806 - root_mean_squared_error: 0.9384 - val_loss: 3.2341 - val_root_mean_squared_error: 1.7984\n",
      "Epoch 15/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.8469 - root_mean_squared_error: 0.9203 - val_loss: 3.1297 - val_root_mean_squared_error: 1.7691\n",
      "Epoch 16/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.8121 - root_mean_squared_error: 0.9011 - val_loss: 3.0257 - val_root_mean_squared_error: 1.7394\n",
      "Epoch 17/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.7787 - root_mean_squared_error: 0.8824 - val_loss: 2.8630 - val_root_mean_squared_error: 1.6920\n",
      "Epoch 18/80\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.7451 - root_mean_squared_error: 0.8632 - val_loss: 2.6675 - val_root_mean_squared_error: 1.6332\n",
      "Epoch 19/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.7130 - root_mean_squared_error: 0.8444 - val_loss: 2.4381 - val_root_mean_squared_error: 1.5614\n",
      "Epoch 20/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.6765 - root_mean_squared_error: 0.8225 - val_loss: 2.2283 - val_root_mean_squared_error: 1.4927\n",
      "Epoch 21/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.6413 - root_mean_squared_error: 0.8008 - val_loss: 2.0634 - val_root_mean_squared_error: 1.4364\n",
      "Epoch 22/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.6064 - root_mean_squared_error: 0.7787 - val_loss: 1.8845 - val_root_mean_squared_error: 1.3728\n",
      "Epoch 23/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.5689 - root_mean_squared_error: 0.7543 - val_loss: 1.7502 - val_root_mean_squared_error: 1.3230\n",
      "Epoch 24/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.5315 - root_mean_squared_error: 0.7290 - val_loss: 1.5643 - val_root_mean_squared_error: 1.2507\n",
      "Epoch 25/80\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.4959 - root_mean_squared_error: 0.7042 - val_loss: 1.4457 - val_root_mean_squared_error: 1.2024\n",
      "Epoch 26/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.4625 - root_mean_squared_error: 0.6801 - val_loss: 1.2312 - val_root_mean_squared_error: 1.1096\n",
      "Epoch 27/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.4283 - root_mean_squared_error: 0.6544 - val_loss: 1.0658 - val_root_mean_squared_error: 1.0324\n",
      "Epoch 28/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.3973 - root_mean_squared_error: 0.6303 - val_loss: 0.9570 - val_root_mean_squared_error: 0.9782\n",
      "Epoch 29/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.3701 - root_mean_squared_error: 0.6084 - val_loss: 0.8344 - val_root_mean_squared_error: 0.9135\n",
      "Epoch 30/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.3409 - root_mean_squared_error: 0.5838 - val_loss: 0.7148 - val_root_mean_squared_error: 0.8454\n",
      "Epoch 31/80\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.3135 - root_mean_squared_error: 0.5599 - val_loss: 0.6403 - val_root_mean_squared_error: 0.8002\n",
      "Epoch 32/80\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2915 - root_mean_squared_error: 0.5399 - val_loss: 0.5329 - val_root_mean_squared_error: 0.7300\n",
      "Epoch 33/80\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2692 - root_mean_squared_error: 0.5189 - val_loss: 0.4380 - val_root_mean_squared_error: 0.6618\n",
      "Epoch 34/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.2519 - root_mean_squared_error: 0.5019 - val_loss: 0.3845 - val_root_mean_squared_error: 0.6201\n",
      "Epoch 35/80\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2344 - root_mean_squared_error: 0.4841 - val_loss: 0.3471 - val_root_mean_squared_error: 0.5891\n",
      "Epoch 36/80\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2206 - root_mean_squared_error: 0.4697 - val_loss: 0.2960 - val_root_mean_squared_error: 0.5440\n",
      "Epoch 37/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.2098 - root_mean_squared_error: 0.4580 - val_loss: 0.2774 - val_root_mean_squared_error: 0.5267\n",
      "Epoch 38/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1957 - root_mean_squared_error: 0.4424 - val_loss: 0.2302 - val_root_mean_squared_error: 0.4798\n",
      "Epoch 39/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1870 - root_mean_squared_error: 0.4325 - val_loss: 0.2170 - val_root_mean_squared_error: 0.4659\n",
      "Epoch 40/80\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.1789 - root_mean_squared_error: 0.4229 - val_loss: 0.2243 - val_root_mean_squared_error: 0.4736\n",
      "Epoch 41/80\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1736 - root_mean_squared_error: 0.4167 - val_loss: 0.1996 - val_root_mean_squared_error: 0.4467\n",
      "Epoch 42/80\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1598 - root_mean_squared_error: 0.3997 - val_loss: 0.1819 - val_root_mean_squared_error: 0.4265\n",
      "Epoch 43/80\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1594 - root_mean_squared_error: 0.3992 - val_loss: 0.1782 - val_root_mean_squared_error: 0.4221\n",
      "Epoch 44/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1509 - root_mean_squared_error: 0.3885 - val_loss: 0.1783 - val_root_mean_squared_error: 0.4222\n",
      "Epoch 45/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1436 - root_mean_squared_error: 0.3790 - val_loss: 0.1733 - val_root_mean_squared_error: 0.4163\n",
      "Epoch 46/80\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1399 - root_mean_squared_error: 0.3740 - val_loss: 0.1772 - val_root_mean_squared_error: 0.4210\n",
      "Epoch 47/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1317 - root_mean_squared_error: 0.3629 - val_loss: 0.1714 - val_root_mean_squared_error: 0.4140\n",
      "Epoch 48/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1271 - root_mean_squared_error: 0.3566 - val_loss: 0.1698 - val_root_mean_squared_error: 0.4120\n",
      "Epoch 49/80\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1214 - root_mean_squared_error: 0.3485 - val_loss: 0.1749 - val_root_mean_squared_error: 0.4182\n",
      "Epoch 50/80\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1156 - root_mean_squared_error: 0.3400 - val_loss: 0.1728 - val_root_mean_squared_error: 0.4157\n",
      "Epoch 51/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1133 - root_mean_squared_error: 0.3366 - val_loss: 0.1689 - val_root_mean_squared_error: 0.4110\n",
      "Epoch 52/80\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1066 - root_mean_squared_error: 0.3264 - val_loss: 0.1834 - val_root_mean_squared_error: 0.4282\n",
      "Epoch 53/80\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1049 - root_mean_squared_error: 0.3239 - val_loss: 0.1837 - val_root_mean_squared_error: 0.4286\n",
      "Epoch 54/80\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0991 - root_mean_squared_error: 0.3147 - val_loss: 0.1715 - val_root_mean_squared_error: 0.4141\n",
      "Epoch 55/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0944 - root_mean_squared_error: 0.3073 - val_loss: 0.1800 - val_root_mean_squared_error: 0.4243\n",
      "Epoch 56/80\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0906 - root_mean_squared_error: 0.3010 - val_loss: 0.1790 - val_root_mean_squared_error: 0.4231\n",
      "Epoch 57/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0890 - root_mean_squared_error: 0.2984 - val_loss: 0.1688 - val_root_mean_squared_error: 0.4109\n",
      "Epoch 58/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0848 - root_mean_squared_error: 0.2911 - val_loss: 0.1878 - val_root_mean_squared_error: 0.4333\n",
      "Epoch 59/80\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0789 - root_mean_squared_error: 0.2809 - val_loss: 0.1737 - val_root_mean_squared_error: 0.4168\n",
      "Epoch 60/80\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0756 - root_mean_squared_error: 0.2750 - val_loss: 0.1723 - val_root_mean_squared_error: 0.4151\n",
      "Epoch 61/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0716 - root_mean_squared_error: 0.2677 - val_loss: 0.1816 - val_root_mean_squared_error: 0.4261\n",
      "Epoch 62/80\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0689 - root_mean_squared_error: 0.2625 - val_loss: 0.1828 - val_root_mean_squared_error: 0.4275\n",
      "Epoch 63/80\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0650 - root_mean_squared_error: 0.2550 - val_loss: 0.1781 - val_root_mean_squared_error: 0.4220\n",
      "Epoch 64/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0618 - root_mean_squared_error: 0.2486 - val_loss: 0.1776 - val_root_mean_squared_error: 0.4214\n",
      "Epoch 65/80\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0590 - root_mean_squared_error: 0.2429 - val_loss: 0.1777 - val_root_mean_squared_error: 0.4215\n",
      "Epoch 66/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0558 - root_mean_squared_error: 0.2362 - val_loss: 0.1784 - val_root_mean_squared_error: 0.4224\n",
      "Epoch 67/80\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0528 - root_mean_squared_error: 0.2298 - val_loss: 0.1859 - val_root_mean_squared_error: 0.4311\n",
      "Epoch 68/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0503 - root_mean_squared_error: 0.2243 - val_loss: 0.1878 - val_root_mean_squared_error: 0.4333\n",
      "Epoch 69/80\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0472 - root_mean_squared_error: 0.2173 - val_loss: 0.1803 - val_root_mean_squared_error: 0.4246\n",
      "Epoch 70/80\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0448 - root_mean_squared_error: 0.2117 - val_loss: 0.1836 - val_root_mean_squared_error: 0.4285\n",
      "Epoch 71/80\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0423 - root_mean_squared_error: 0.2058 - val_loss: 0.1887 - val_root_mean_squared_error: 0.4344\n",
      "Epoch 72/80\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0401 - root_mean_squared_error: 0.2004 - val_loss: 0.1851 - val_root_mean_squared_error: 0.4302\n",
      "Epoch 73/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0380 - root_mean_squared_error: 0.1948 - val_loss: 0.1899 - val_root_mean_squared_error: 0.4358\n",
      "Epoch 74/80\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0360 - root_mean_squared_error: 0.1898 - val_loss: 0.1972 - val_root_mean_squared_error: 0.4440\n",
      "Epoch 75/80\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0348 - root_mean_squared_error: 0.1866 - val_loss: 0.1928 - val_root_mean_squared_error: 0.4391\n",
      "Epoch 76/80\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0331 - root_mean_squared_error: 0.1818 - val_loss: 0.1873 - val_root_mean_squared_error: 0.4328\n",
      "Epoch 77/80\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0334 - root_mean_squared_error: 0.1827 - val_loss: 0.1924 - val_root_mean_squared_error: 0.4386\n",
      "Epoch 78/80\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0304 - root_mean_squared_error: 0.1742 - val_loss: 0.1837 - val_root_mean_squared_error: 0.4286\n",
      "Epoch 79/80\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0279 - root_mean_squared_error: 0.1670 - val_loss: 0.1973 - val_root_mean_squared_error: 0.4442\n",
      "Epoch 80/80\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0271 - root_mean_squared_error: 0.1647 - val_loss: 0.1917 - val_root_mean_squared_error: 0.4378\n"
     ]
    }
   ],
   "source": [
    "# LSTM (before Omicron)\n",
    "model_before_lstm = Sequential()\n",
    "model_before_lstm.add(InputLayer((7, 8)))\n",
    "model_cnn.add(Conv1D(32, kernel_size=2))\n",
    "\n",
    "# model_before_lstm.add(Dropout(0.2))\n",
    "# model_before_lstm.add(Dense(8, 'relu'))\n",
    "model_before_lstm.add(Dense(1, 'linear'))\n",
    "\n",
    "# model_Multivariate.build(input_shape=(None, 64, 8, 1))\n",
    "# model_Multivariate.summary()\n",
    "\n",
    "# cp_lstm = ModelCheckpoint('model_lstm/', save_best_only=True)\n",
    "model_before_lstm.compile(loss=MeanSquaredError(), optimizer=Adam(learning_rate=0.0001), metrics=[RootMeanSquaredError()])\n",
    "history = model_before_lstm.fit(X2_before_train, y2_before_train, batch_size=32, validation_data=(X2_before_val, y2_before_val), epochs=80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1593,
   "id": "9b09ae0c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/120\n",
      "9/9 [==============================] - 1s 18ms/step - loss: 323.0245 - root_mean_squared_error: 17.9729 - val_loss: 402.0359 - val_root_mean_squared_error: 20.0508\n",
      "Epoch 2/120\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 302.0303 - root_mean_squared_error: 17.3790 - val_loss: 376.1597 - val_root_mean_squared_error: 19.3948\n",
      "Epoch 3/120\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 281.6434 - root_mean_squared_error: 16.7822 - val_loss: 350.1585 - val_root_mean_squared_error: 18.7125\n",
      "Epoch 4/120\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 261.5541 - root_mean_squared_error: 16.1726 - val_loss: 324.1928 - val_root_mean_squared_error: 18.0054\n",
      "Epoch 5/120\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 241.5137 - root_mean_squared_error: 15.5407 - val_loss: 297.6614 - val_root_mean_squared_error: 17.2529\n",
      "Epoch 6/120\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 221.0278 - root_mean_squared_error: 14.8670 - val_loss: 270.7302 - val_root_mean_squared_error: 16.4539\n",
      "Epoch 7/120\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 200.1256 - root_mean_squared_error: 14.1466 - val_loss: 241.9695 - val_root_mean_squared_error: 15.5554\n",
      "Epoch 8/120\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 177.8786 - root_mean_squared_error: 13.3371 - val_loss: 211.2249 - val_root_mean_squared_error: 14.5336\n",
      "Epoch 9/120\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 154.1657 - root_mean_squared_error: 12.4163 - val_loss: 177.9166 - val_root_mean_squared_error: 13.3385\n",
      "Epoch 10/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 128.6889 - root_mean_squared_error: 11.3441 - val_loss: 140.7927 - val_root_mean_squared_error: 11.8656\n",
      "Epoch 11/120\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 100.8731 - root_mean_squared_error: 10.0436 - val_loss: 100.2046 - val_root_mean_squared_error: 10.0102\n",
      "Epoch 12/120\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 71.8559 - root_mean_squared_error: 8.4768 - val_loss: 58.7249 - val_root_mean_squared_error: 7.6632\n",
      "Epoch 13/120\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 45.1434 - root_mean_squared_error: 6.7189 - val_loss: 23.7580 - val_root_mean_squared_error: 4.8742\n",
      "Epoch 14/120\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 26.6327 - root_mean_squared_error: 5.1607 - val_loss: 4.6273 - val_root_mean_squared_error: 2.1511\n",
      "Epoch 15/120\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 19.4562 - root_mean_squared_error: 4.4109 - val_loss: 1.2991 - val_root_mean_squared_error: 1.1398\n",
      "Epoch 16/120\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 17.1948 - root_mean_squared_error: 4.1467 - val_loss: 1.2957 - val_root_mean_squared_error: 1.1383\n",
      "Epoch 17/120\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 13.9480 - root_mean_squared_error: 3.7347 - val_loss: 1.8629 - val_root_mean_squared_error: 1.3649\n",
      "Epoch 18/120\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 11.1055 - root_mean_squared_error: 3.3325 - val_loss: 2.4000 - val_root_mean_squared_error: 1.5492\n",
      "Epoch 19/120\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 8.5808 - root_mean_squared_error: 2.9293 - val_loss: 2.6644 - val_root_mean_squared_error: 1.6323\n",
      "Epoch 20/120\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 6.6631 - root_mean_squared_error: 2.5813 - val_loss: 2.8059 - val_root_mean_squared_error: 1.6751\n",
      "Epoch 21/120\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 5.2812 - root_mean_squared_error: 2.2981 - val_loss: 2.6827 - val_root_mean_squared_error: 1.6379\n",
      "Epoch 22/120\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 4.7646 - root_mean_squared_error: 2.1828 - val_loss: 3.1218 - val_root_mean_squared_error: 1.7669\n",
      "Epoch 23/120\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 4.4190 - root_mean_squared_error: 2.1022 - val_loss: 3.5559 - val_root_mean_squared_error: 1.8857\n",
      "Epoch 24/120\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 4.1300 - root_mean_squared_error: 2.0322 - val_loss: 3.1751 - val_root_mean_squared_error: 1.7819\n",
      "Epoch 25/120\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 3.8883 - root_mean_squared_error: 1.9719 - val_loss: 2.9265 - val_root_mean_squared_error: 1.7107\n",
      "Epoch 26/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 3.6226 - root_mean_squared_error: 1.9033 - val_loss: 2.9213 - val_root_mean_squared_error: 1.7092\n",
      "Epoch 27/120\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 3.3695 - root_mean_squared_error: 1.8356 - val_loss: 2.5362 - val_root_mean_squared_error: 1.5925\n",
      "Epoch 28/120\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 3.1624 - root_mean_squared_error: 1.7783 - val_loss: 2.4540 - val_root_mean_squared_error: 1.5665\n",
      "Epoch 29/120\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 2.9467 - root_mean_squared_error: 1.7166 - val_loss: 2.4658 - val_root_mean_squared_error: 1.5703\n",
      "Epoch 30/120\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 2.7493 - root_mean_squared_error: 1.6581 - val_loss: 2.2783 - val_root_mean_squared_error: 1.5094\n",
      "Epoch 31/120\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 2.5755 - root_mean_squared_error: 1.6048 - val_loss: 2.1076 - val_root_mean_squared_error: 1.4518\n",
      "Epoch 32/120\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 2.3979 - root_mean_squared_error: 1.5485 - val_loss: 2.1847 - val_root_mean_squared_error: 1.4781\n",
      "Epoch 33/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 2.2432 - root_mean_squared_error: 1.4977 - val_loss: 2.2717 - val_root_mean_squared_error: 1.5072\n",
      "Epoch 34/120\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 2.0830 - root_mean_squared_error: 1.4433 - val_loss: 1.9788 - val_root_mean_squared_error: 1.4067\n",
      "Epoch 35/120\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 1.9136 - root_mean_squared_error: 1.3833 - val_loss: 1.6794 - val_root_mean_squared_error: 1.2959\n",
      "Epoch 36/120\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 1.7676 - root_mean_squared_error: 1.3295 - val_loss: 1.6778 - val_root_mean_squared_error: 1.2953\n",
      "Epoch 37/120\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 1.6367 - root_mean_squared_error: 1.2793 - val_loss: 1.8651 - val_root_mean_squared_error: 1.3657\n",
      "Epoch 38/120\n",
      "9/9 [==============================] - 0s 5ms/step - loss: 1.5267 - root_mean_squared_error: 1.2356 - val_loss: 1.7102 - val_root_mean_squared_error: 1.3077\n",
      "Epoch 39/120\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 1.3897 - root_mean_squared_error: 1.1788 - val_loss: 1.2612 - val_root_mean_squared_error: 1.1230\n",
      "Epoch 40/120\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 1.2932 - root_mean_squared_error: 1.1372 - val_loss: 1.1282 - val_root_mean_squared_error: 1.0622\n",
      "Epoch 41/120\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 1.2013 - root_mean_squared_error: 1.0961 - val_loss: 1.0712 - val_root_mean_squared_error: 1.0350\n",
      "Epoch 42/120\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 1.1069 - root_mean_squared_error: 1.0521 - val_loss: 1.1584 - val_root_mean_squared_error: 1.0763\n",
      "Epoch 43/120\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 1.0146 - root_mean_squared_error: 1.0073 - val_loss: 1.1660 - val_root_mean_squared_error: 1.0798\n",
      "Epoch 44/120\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.9493 - root_mean_squared_error: 0.9743 - val_loss: 1.1535 - val_root_mean_squared_error: 1.0740\n",
      "Epoch 45/120\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.8845 - root_mean_squared_error: 0.9405 - val_loss: 1.1476 - val_root_mean_squared_error: 1.0712\n",
      "Epoch 46/120\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.8164 - root_mean_squared_error: 0.9035 - val_loss: 0.9262 - val_root_mean_squared_error: 0.9624\n",
      "Epoch 47/120\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.7522 - root_mean_squared_error: 0.8673 - val_loss: 0.8810 - val_root_mean_squared_error: 0.9386\n",
      "Epoch 48/120\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.6955 - root_mean_squared_error: 0.8340 - val_loss: 0.8038 - val_root_mean_squared_error: 0.8966\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 49/120\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.6524 - root_mean_squared_error: 0.8077 - val_loss: 0.7046 - val_root_mean_squared_error: 0.8394\n",
      "Epoch 50/120\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.6146 - root_mean_squared_error: 0.7839 - val_loss: 0.6235 - val_root_mean_squared_error: 0.7896\n",
      "Epoch 51/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.5618 - root_mean_squared_error: 0.7495 - val_loss: 0.7362 - val_root_mean_squared_error: 0.8580\n",
      "Epoch 52/120\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.5311 - root_mean_squared_error: 0.7288 - val_loss: 0.6177 - val_root_mean_squared_error: 0.7860\n",
      "Epoch 53/120\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.4929 - root_mean_squared_error: 0.7021 - val_loss: 0.5550 - val_root_mean_squared_error: 0.7450\n",
      "Epoch 54/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.4715 - root_mean_squared_error: 0.6866 - val_loss: 0.4671 - val_root_mean_squared_error: 0.6835\n",
      "Epoch 55/120\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.4374 - root_mean_squared_error: 0.6614 - val_loss: 0.5599 - val_root_mean_squared_error: 0.7482\n",
      "Epoch 56/120\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.4138 - root_mean_squared_error: 0.6433 - val_loss: 0.4666 - val_root_mean_squared_error: 0.6831\n",
      "Epoch 57/120\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.3904 - root_mean_squared_error: 0.6248 - val_loss: 0.4095 - val_root_mean_squared_error: 0.6400\n",
      "Epoch 58/120\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.3700 - root_mean_squared_error: 0.6083 - val_loss: 0.4044 - val_root_mean_squared_error: 0.6359\n",
      "Epoch 59/120\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.3570 - root_mean_squared_error: 0.5975 - val_loss: 0.3808 - val_root_mean_squared_error: 0.6171\n",
      "Epoch 60/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.3372 - root_mean_squared_error: 0.5807 - val_loss: 0.2700 - val_root_mean_squared_error: 0.5196\n",
      "Epoch 61/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.3265 - root_mean_squared_error: 0.5714 - val_loss: 0.2856 - val_root_mean_squared_error: 0.5344\n",
      "Epoch 62/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.3084 - root_mean_squared_error: 0.5554 - val_loss: 0.2954 - val_root_mean_squared_error: 0.5435\n",
      "Epoch 63/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.2968 - root_mean_squared_error: 0.5448 - val_loss: 0.3201 - val_root_mean_squared_error: 0.5658\n",
      "Epoch 64/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.2854 - root_mean_squared_error: 0.5342 - val_loss: 0.2911 - val_root_mean_squared_error: 0.5395\n",
      "Epoch 65/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.2758 - root_mean_squared_error: 0.5251 - val_loss: 0.2605 - val_root_mean_squared_error: 0.5104\n",
      "Epoch 66/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.2671 - root_mean_squared_error: 0.5168 - val_loss: 0.2534 - val_root_mean_squared_error: 0.5033\n",
      "Epoch 67/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.2579 - root_mean_squared_error: 0.5078 - val_loss: 0.2009 - val_root_mean_squared_error: 0.4482\n",
      "Epoch 68/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.2592 - root_mean_squared_error: 0.5092 - val_loss: 0.1792 - val_root_mean_squared_error: 0.4233\n",
      "Epoch 69/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.2475 - root_mean_squared_error: 0.4975 - val_loss: 0.2251 - val_root_mean_squared_error: 0.4745\n",
      "Epoch 70/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.2397 - root_mean_squared_error: 0.4896 - val_loss: 0.1728 - val_root_mean_squared_error: 0.4157\n",
      "Epoch 71/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.2312 - root_mean_squared_error: 0.4808 - val_loss: 0.1762 - val_root_mean_squared_error: 0.4197\n",
      "Epoch 72/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.2246 - root_mean_squared_error: 0.4739 - val_loss: 0.1938 - val_root_mean_squared_error: 0.4403\n",
      "Epoch 73/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.2177 - root_mean_squared_error: 0.4666 - val_loss: 0.1662 - val_root_mean_squared_error: 0.4076\n",
      "Epoch 74/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.2139 - root_mean_squared_error: 0.4625 - val_loss: 0.1641 - val_root_mean_squared_error: 0.4050\n",
      "Epoch 75/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.2073 - root_mean_squared_error: 0.4552 - val_loss: 0.1531 - val_root_mean_squared_error: 0.3913\n",
      "Epoch 76/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.2025 - root_mean_squared_error: 0.4500 - val_loss: 0.1521 - val_root_mean_squared_error: 0.3899\n",
      "Epoch 77/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1981 - root_mean_squared_error: 0.4451 - val_loss: 0.1447 - val_root_mean_squared_error: 0.3804\n",
      "Epoch 78/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1942 - root_mean_squared_error: 0.4407 - val_loss: 0.1415 - val_root_mean_squared_error: 0.3761\n",
      "Epoch 79/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1902 - root_mean_squared_error: 0.4361 - val_loss: 0.1402 - val_root_mean_squared_error: 0.3745\n",
      "Epoch 80/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1857 - root_mean_squared_error: 0.4309 - val_loss: 0.1309 - val_root_mean_squared_error: 0.3618\n",
      "Epoch 81/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1844 - root_mean_squared_error: 0.4294 - val_loss: 0.1366 - val_root_mean_squared_error: 0.3697\n",
      "Epoch 82/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1795 - root_mean_squared_error: 0.4237 - val_loss: 0.1209 - val_root_mean_squared_error: 0.3477\n",
      "Epoch 83/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1754 - root_mean_squared_error: 0.4188 - val_loss: 0.1169 - val_root_mean_squared_error: 0.3419\n",
      "Epoch 84/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1705 - root_mean_squared_error: 0.4129 - val_loss: 0.1150 - val_root_mean_squared_error: 0.3391\n",
      "Epoch 85/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1675 - root_mean_squared_error: 0.4092 - val_loss: 0.1114 - val_root_mean_squared_error: 0.3338\n",
      "Epoch 86/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1631 - root_mean_squared_error: 0.4038 - val_loss: 0.1103 - val_root_mean_squared_error: 0.3321\n",
      "Epoch 87/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1600 - root_mean_squared_error: 0.4000 - val_loss: 0.1086 - val_root_mean_squared_error: 0.3296\n",
      "Epoch 88/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1592 - root_mean_squared_error: 0.3990 - val_loss: 0.1115 - val_root_mean_squared_error: 0.3339\n",
      "Epoch 89/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1536 - root_mean_squared_error: 0.3919 - val_loss: 0.1021 - val_root_mean_squared_error: 0.3195\n",
      "Epoch 90/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1527 - root_mean_squared_error: 0.3907 - val_loss: 0.1038 - val_root_mean_squared_error: 0.3221\n",
      "Epoch 91/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1488 - root_mean_squared_error: 0.3858 - val_loss: 0.0984 - val_root_mean_squared_error: 0.3137\n",
      "Epoch 92/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1447 - root_mean_squared_error: 0.3804 - val_loss: 0.0964 - val_root_mean_squared_error: 0.3104\n",
      "Epoch 93/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1415 - root_mean_squared_error: 0.3761 - val_loss: 0.0945 - val_root_mean_squared_error: 0.3074\n",
      "Epoch 94/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1410 - root_mean_squared_error: 0.3755 - val_loss: 0.0937 - val_root_mean_squared_error: 0.3060\n",
      "Epoch 95/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1369 - root_mean_squared_error: 0.3700 - val_loss: 0.0914 - val_root_mean_squared_error: 0.3024\n",
      "Epoch 96/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1349 - root_mean_squared_error: 0.3673 - val_loss: 0.0944 - val_root_mean_squared_error: 0.3073\n",
      "Epoch 97/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1314 - root_mean_squared_error: 0.3624 - val_loss: 0.0882 - val_root_mean_squared_error: 0.2971\n",
      "Epoch 98/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1296 - root_mean_squared_error: 0.3599 - val_loss: 0.0861 - val_root_mean_squared_error: 0.2934\n",
      "Epoch 99/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1248 - root_mean_squared_error: 0.3533 - val_loss: 0.0839 - val_root_mean_squared_error: 0.2896\n",
      "Epoch 100/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1230 - root_mean_squared_error: 0.3507 - val_loss: 0.0818 - val_root_mean_squared_error: 0.2859\n",
      "Epoch 101/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1200 - root_mean_squared_error: 0.3464 - val_loss: 0.0801 - val_root_mean_squared_error: 0.2829\n",
      "Epoch 102/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1181 - root_mean_squared_error: 0.3436 - val_loss: 0.0786 - val_root_mean_squared_error: 0.2803\n",
      "Epoch 103/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1159 - root_mean_squared_error: 0.3405 - val_loss: 0.0772 - val_root_mean_squared_error: 0.2778\n",
      "Epoch 104/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1131 - root_mean_squared_error: 0.3364 - val_loss: 0.0775 - val_root_mean_squared_error: 0.2783\n",
      "Epoch 105/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1109 - root_mean_squared_error: 0.3330 - val_loss: 0.0745 - val_root_mean_squared_error: 0.2729\n",
      "Epoch 106/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1099 - root_mean_squared_error: 0.3314 - val_loss: 0.0772 - val_root_mean_squared_error: 0.2778\n",
      "Epoch 107/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1070 - root_mean_squared_error: 0.3270 - val_loss: 0.0724 - val_root_mean_squared_error: 0.2690\n",
      "Epoch 108/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1044 - root_mean_squared_error: 0.3230 - val_loss: 0.0726 - val_root_mean_squared_error: 0.2695\n",
      "Epoch 109/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1023 - root_mean_squared_error: 0.3198 - val_loss: 0.0715 - val_root_mean_squared_error: 0.2673\n",
      "Epoch 110/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1001 - root_mean_squared_error: 0.3164 - val_loss: 0.0689 - val_root_mean_squared_error: 0.2624\n",
      "Epoch 111/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0991 - root_mean_squared_error: 0.3148 - val_loss: 0.0679 - val_root_mean_squared_error: 0.2606\n",
      "Epoch 112/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0969 - root_mean_squared_error: 0.3113 - val_loss: 0.0674 - val_root_mean_squared_error: 0.2596\n",
      "Epoch 113/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0956 - root_mean_squared_error: 0.3091 - val_loss: 0.0667 - val_root_mean_squared_error: 0.2583\n",
      "Epoch 114/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0941 - root_mean_squared_error: 0.3067 - val_loss: 0.0632 - val_root_mean_squared_error: 0.2513\n",
      "Epoch 115/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0913 - root_mean_squared_error: 0.3022 - val_loss: 0.0662 - val_root_mean_squared_error: 0.2572\n",
      "Epoch 116/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0894 - root_mean_squared_error: 0.2991 - val_loss: 0.0631 - val_root_mean_squared_error: 0.2513\n",
      "Epoch 117/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0878 - root_mean_squared_error: 0.2963 - val_loss: 0.0627 - val_root_mean_squared_error: 0.2504\n",
      "Epoch 118/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0884 - root_mean_squared_error: 0.2973 - val_loss: 0.0696 - val_root_mean_squared_error: 0.2638\n",
      "Epoch 119/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0852 - root_mean_squared_error: 0.2919 - val_loss: 0.0587 - val_root_mean_squared_error: 0.2423\n",
      "Epoch 120/120\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0832 - root_mean_squared_error: 0.2885 - val_loss: 0.0591 - val_root_mean_squared_error: 0.2431\n"
     ]
    }
   ],
   "source": [
    "# BiLSTM\n",
    "model_BiLSTM = Sequential()\n",
    "model_BiLSTM.add(InputLayer((7, 8)))\n",
    "model_BiLSTM.add(Bidirectional(LSTM(32, activation='relu', input_shape=(X2_train.shape[1], X2_train.shape[2]),return_sequences=False)))\n",
    "# model_BiLSTM.add(Dense(8, 'relu'))\n",
    "# model_BiLSTM.add(Dropout(0.2))\n",
    "model_BiLSTM.add(Dense(1, 'linear'))\n",
    "\n",
    "# model_Multivariate.build(input_shape=(None, 64, 8, 1))\n",
    "# model_Multivariate.summary()\n",
    "\n",
    "# cp_lstm = ModelCheckpoint('model_lstm/', save_best_only=True)\n",
    "model_BiLSTM.compile(loss=MeanSquaredError(), optimizer=Adam(learning_rate=0.0001), metrics=[RootMeanSquaredError()])\n",
    "history = model_BiLSTM.fit(X2_train, y2_train, batch_size=32, validation_data=(X2_val, y2_val), epochs=120)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1375,
   "id": "e1f18aff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/180\n",
      "9/9 [==============================] - 1s 17ms/step - loss: 31.5884 - root_mean_squared_error: 5.6204 - val_loss: 13.4749 - val_root_mean_squared_error: 3.6708\n",
      "Epoch 2/180\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 28.7793 - root_mean_squared_error: 5.3646 - val_loss: 12.0243 - val_root_mean_squared_error: 3.4676\n",
      "Epoch 3/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 26.1741 - root_mean_squared_error: 5.1161 - val_loss: 10.6821 - val_root_mean_squared_error: 3.2684\n",
      "Epoch 4/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 23.7365 - root_mean_squared_error: 4.8720 - val_loss: 9.4326 - val_root_mean_squared_error: 3.0712\n",
      "Epoch 5/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 21.4326 - root_mean_squared_error: 4.6295 - val_loss: 8.2078 - val_root_mean_squared_error: 2.8649\n",
      "Epoch 6/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 19.1874 - root_mean_squared_error: 4.3803 - val_loss: 7.0541 - val_root_mean_squared_error: 2.6559\n",
      "Epoch 7/180\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 17.0497 - root_mean_squared_error: 4.1291 - val_loss: 5.9368 - val_root_mean_squared_error: 2.4365\n",
      "Epoch 8/180\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 14.9341 - root_mean_squared_error: 3.8645 - val_loss: 4.8758 - val_root_mean_squared_error: 2.2081\n",
      "Epoch 9/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 12.8608 - root_mean_squared_error: 3.5862 - val_loss: 3.8860 - val_root_mean_squared_error: 1.9713\n",
      "Epoch 10/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 10.8191 - root_mean_squared_error: 3.2892 - val_loss: 2.9451 - val_root_mean_squared_error: 1.7161\n",
      "Epoch 11/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 8.8199 - root_mean_squared_error: 2.9698 - val_loss: 2.0981 - val_root_mean_squared_error: 1.4485\n",
      "Epoch 12/180\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 6.9185 - root_mean_squared_error: 2.6303 - val_loss: 1.3603 - val_root_mean_squared_error: 1.1663\n",
      "Epoch 13/180\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 5.1700 - root_mean_squared_error: 2.2738 - val_loss: 0.7837 - val_root_mean_squared_error: 0.8853\n",
      "Epoch 14/180\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 3.6601 - root_mean_squared_error: 1.9131 - val_loss: 0.4106 - val_root_mean_squared_error: 0.6408\n",
      "Epoch 15/180\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 2.4382 - root_mean_squared_error: 1.5615 - val_loss: 0.2427 - val_root_mean_squared_error: 0.4926\n",
      "Epoch 16/180\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 1.6051 - root_mean_squared_error: 1.2669 - val_loss: 0.2510 - val_root_mean_squared_error: 0.5010\n",
      "Epoch 17/180\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 1.1206 - root_mean_squared_error: 1.0586 - val_loss: 0.3702 - val_root_mean_squared_error: 0.6085\n",
      "Epoch 18/180\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.8846 - root_mean_squared_error: 0.9405 - val_loss: 0.4828 - val_root_mean_squared_error: 0.6948\n",
      "Epoch 19/180\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.8002 - root_mean_squared_error: 0.8945 - val_loss: 0.5313 - val_root_mean_squared_error: 0.7289\n",
      "Epoch 20/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.7426 - root_mean_squared_error: 0.8617 - val_loss: 0.5146 - val_root_mean_squared_error: 0.7173\n",
      "Epoch 21/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.6992 - root_mean_squared_error: 0.8362 - val_loss: 0.4853 - val_root_mean_squared_error: 0.6967\n",
      "Epoch 22/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.6550 - root_mean_squared_error: 0.8093 - val_loss: 0.4495 - val_root_mean_squared_error: 0.6705\n",
      "Epoch 23/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.6174 - root_mean_squared_error: 0.7857 - val_loss: 0.4192 - val_root_mean_squared_error: 0.6474\n",
      "Epoch 24/180\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.5855 - root_mean_squared_error: 0.7652 - val_loss: 0.4109 - val_root_mean_squared_error: 0.6410\n",
      "Epoch 25/180\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.5601 - root_mean_squared_error: 0.7484 - val_loss: 0.3960 - val_root_mean_squared_error: 0.6293\n",
      "Epoch 26/180\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.5337 - root_mean_squared_error: 0.7306 - val_loss: 0.3770 - val_root_mean_squared_error: 0.6140\n",
      "Epoch 27/180\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.5143 - root_mean_squared_error: 0.7172 - val_loss: 0.3678 - val_root_mean_squared_error: 0.6065\n",
      "Epoch 28/180\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.4991 - root_mean_squared_error: 0.7065 - val_loss: 0.3404 - val_root_mean_squared_error: 0.5835\n",
      "Epoch 29/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.4824 - root_mean_squared_error: 0.6945 - val_loss: 0.3315 - val_root_mean_squared_error: 0.5757\n",
      "Epoch 30/180\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.4677 - root_mean_squared_error: 0.6839 - val_loss: 0.3422 - val_root_mean_squared_error: 0.5850\n",
      "Epoch 31/180\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.4523 - root_mean_squared_error: 0.6725 - val_loss: 0.3468 - val_root_mean_squared_error: 0.5889\n",
      "Epoch 32/180\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.4372 - root_mean_squared_error: 0.6612 - val_loss: 0.3567 - val_root_mean_squared_error: 0.5973\n",
      "Epoch 33/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.4240 - root_mean_squared_error: 0.6512 - val_loss: 0.3715 - val_root_mean_squared_error: 0.6095\n",
      "Epoch 34/180\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.4103 - root_mean_squared_error: 0.6405 - val_loss: 0.3750 - val_root_mean_squared_error: 0.6124\n",
      "Epoch 35/180\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.3967 - root_mean_squared_error: 0.6298 - val_loss: 0.3819 - val_root_mean_squared_error: 0.6180\n",
      "Epoch 36/180\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.3836 - root_mean_squared_error: 0.6193 - val_loss: 0.3890 - val_root_mean_squared_error: 0.6237\n",
      "Epoch 37/180\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.3704 - root_mean_squared_error: 0.6086 - val_loss: 0.3799 - val_root_mean_squared_error: 0.6164\n",
      "Epoch 38/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.3581 - root_mean_squared_error: 0.5984 - val_loss: 0.3846 - val_root_mean_squared_error: 0.6202\n",
      "Epoch 39/180\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.3465 - root_mean_squared_error: 0.5887 - val_loss: 0.4010 - val_root_mean_squared_error: 0.6332\n",
      "Epoch 40/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.3339 - root_mean_squared_error: 0.5778 - val_loss: 0.4151 - val_root_mean_squared_error: 0.6443\n",
      "Epoch 41/180\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.3231 - root_mean_squared_error: 0.5684 - val_loss: 0.4415 - val_root_mean_squared_error: 0.6644\n",
      "Epoch 42/180\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.3144 - root_mean_squared_error: 0.5608 - val_loss: 0.4733 - val_root_mean_squared_error: 0.6880\n",
      "Epoch 43/180\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.3014 - root_mean_squared_error: 0.5490 - val_loss: 0.4655 - val_root_mean_squared_error: 0.6823\n",
      "Epoch 44/180\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.2919 - root_mean_squared_error: 0.5403 - val_loss: 0.4755 - val_root_mean_squared_error: 0.6895\n",
      "Epoch 45/180\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.2803 - root_mean_squared_error: 0.5295 - val_loss: 0.5072 - val_root_mean_squared_error: 0.7122\n",
      "Epoch 46/180\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.2712 - root_mean_squared_error: 0.5208 - val_loss: 0.5307 - val_root_mean_squared_error: 0.7285\n",
      "Epoch 47/180\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.2620 - root_mean_squared_error: 0.5119 - val_loss: 0.5405 - val_root_mean_squared_error: 0.7352\n",
      "Epoch 48/180\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.2533 - root_mean_squared_error: 0.5033 - val_loss: 0.5596 - val_root_mean_squared_error: 0.7480\n",
      "Epoch 49/180\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.2436 - root_mean_squared_error: 0.4935 - val_loss: 0.5978 - val_root_mean_squared_error: 0.7731\n",
      "Epoch 50/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.2337 - root_mean_squared_error: 0.4834 - val_loss: 0.6420 - val_root_mean_squared_error: 0.8013\n",
      "Epoch 51/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.2228 - root_mean_squared_error: 0.4721 - val_loss: 0.6363 - val_root_mean_squared_error: 0.7977\n",
      "Epoch 52/180\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.2130 - root_mean_squared_error: 0.4615 - val_loss: 0.6134 - val_root_mean_squared_error: 0.7832\n",
      "Epoch 53/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.2019 - root_mean_squared_error: 0.4493 - val_loss: 0.6501 - val_root_mean_squared_error: 0.8063\n",
      "Epoch 54/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1900 - root_mean_squared_error: 0.4359 - val_loss: 0.7108 - val_root_mean_squared_error: 0.8431\n",
      "Epoch 55/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1802 - root_mean_squared_error: 0.4245 - val_loss: 0.7443 - val_root_mean_squared_error: 0.8627\n",
      "Epoch 56/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1703 - root_mean_squared_error: 0.4127 - val_loss: 0.7917 - val_root_mean_squared_error: 0.8898\n",
      "Epoch 57/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1614 - root_mean_squared_error: 0.4017 - val_loss: 0.8164 - val_root_mean_squared_error: 0.9036\n",
      "Epoch 58/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1522 - root_mean_squared_error: 0.3901 - val_loss: 0.8420 - val_root_mean_squared_error: 0.9176\n",
      "Epoch 59/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1446 - root_mean_squared_error: 0.3803 - val_loss: 0.8500 - val_root_mean_squared_error: 0.9220\n",
      "Epoch 60/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1377 - root_mean_squared_error: 0.3710 - val_loss: 0.8679 - val_root_mean_squared_error: 0.9316\n",
      "Epoch 61/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1312 - root_mean_squared_error: 0.3622 - val_loss: 0.9281 - val_root_mean_squared_error: 0.9634\n",
      "Epoch 62/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1245 - root_mean_squared_error: 0.3528 - val_loss: 0.9684 - val_root_mean_squared_error: 0.9841\n",
      "Epoch 63/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1210 - root_mean_squared_error: 0.3479 - val_loss: 0.9808 - val_root_mean_squared_error: 0.9904\n",
      "Epoch 64/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1155 - root_mean_squared_error: 0.3398 - val_loss: 1.0576 - val_root_mean_squared_error: 1.0284\n",
      "Epoch 65/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1105 - root_mean_squared_error: 0.3325 - val_loss: 1.0967 - val_root_mean_squared_error: 1.0472\n",
      "Epoch 66/180\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.1066 - root_mean_squared_error: 0.3265 - val_loss: 1.1061 - val_root_mean_squared_error: 1.0517\n",
      "Epoch 67/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1027 - root_mean_squared_error: 0.3205 - val_loss: 1.1162 - val_root_mean_squared_error: 1.0565\n",
      "Epoch 68/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0990 - root_mean_squared_error: 0.3146 - val_loss: 1.1462 - val_root_mean_squared_error: 1.0706\n",
      "Epoch 69/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0959 - root_mean_squared_error: 0.3098 - val_loss: 1.1864 - val_root_mean_squared_error: 1.0892\n",
      "Epoch 70/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0931 - root_mean_squared_error: 0.3051 - val_loss: 1.1986 - val_root_mean_squared_error: 1.0948\n",
      "Epoch 71/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0897 - root_mean_squared_error: 0.2995 - val_loss: 1.1695 - val_root_mean_squared_error: 1.0815\n",
      "Epoch 72/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0874 - root_mean_squared_error: 0.2957 - val_loss: 1.1650 - val_root_mean_squared_error: 1.0794\n",
      "Epoch 73/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0851 - root_mean_squared_error: 0.2918 - val_loss: 1.2350 - val_root_mean_squared_error: 1.1113\n",
      "Epoch 74/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0817 - root_mean_squared_error: 0.2859 - val_loss: 1.2058 - val_root_mean_squared_error: 1.0981\n",
      "Epoch 75/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0792 - root_mean_squared_error: 0.2815 - val_loss: 1.1702 - val_root_mean_squared_error: 1.0818\n",
      "Epoch 76/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0777 - root_mean_squared_error: 0.2787 - val_loss: 1.1389 - val_root_mean_squared_error: 1.0672\n",
      "Epoch 77/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0749 - root_mean_squared_error: 0.2738 - val_loss: 1.1553 - val_root_mean_squared_error: 1.0748\n",
      "Epoch 78/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0725 - root_mean_squared_error: 0.2693 - val_loss: 1.1357 - val_root_mean_squared_error: 1.0657\n",
      "Epoch 79/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0709 - root_mean_squared_error: 0.2662 - val_loss: 1.0951 - val_root_mean_squared_error: 1.0465\n",
      "Epoch 80/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0683 - root_mean_squared_error: 0.2613 - val_loss: 1.0922 - val_root_mean_squared_error: 1.0451\n",
      "Epoch 81/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0664 - root_mean_squared_error: 0.2577 - val_loss: 1.0583 - val_root_mean_squared_error: 1.0287\n",
      "Epoch 82/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0642 - root_mean_squared_error: 0.2533 - val_loss: 0.9784 - val_root_mean_squared_error: 0.9891\n",
      "Epoch 83/180\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0625 - root_mean_squared_error: 0.2500 - val_loss: 0.9511 - val_root_mean_squared_error: 0.9753\n",
      "Epoch 84/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0604 - root_mean_squared_error: 0.2458 - val_loss: 0.8981 - val_root_mean_squared_error: 0.9477\n",
      "Epoch 85/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0584 - root_mean_squared_error: 0.2417 - val_loss: 0.8489 - val_root_mean_squared_error: 0.9213\n",
      "Epoch 86/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0567 - root_mean_squared_error: 0.2381 - val_loss: 0.7601 - val_root_mean_squared_error: 0.8718\n",
      "Epoch 87/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0549 - root_mean_squared_error: 0.2344 - val_loss: 0.6896 - val_root_mean_squared_error: 0.8304\n",
      "Epoch 88/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0540 - root_mean_squared_error: 0.2324 - val_loss: 0.6530 - val_root_mean_squared_error: 0.8081\n",
      "Epoch 89/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0516 - root_mean_squared_error: 0.2272 - val_loss: 0.6668 - val_root_mean_squared_error: 0.8166\n",
      "Epoch 90/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0506 - root_mean_squared_error: 0.2250 - val_loss: 0.6205 - val_root_mean_squared_error: 0.7877\n",
      "Epoch 91/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0492 - root_mean_squared_error: 0.2219 - val_loss: 0.5158 - val_root_mean_squared_error: 0.7182\n",
      "Epoch 92/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0478 - root_mean_squared_error: 0.2186 - val_loss: 0.4939 - val_root_mean_squared_error: 0.7028\n",
      "Epoch 93/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0462 - root_mean_squared_error: 0.2150 - val_loss: 0.4971 - val_root_mean_squared_error: 0.7051\n",
      "Epoch 94/180\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0451 - root_mean_squared_error: 0.2123 - val_loss: 0.4419 - val_root_mean_squared_error: 0.6648\n",
      "Epoch 95/180\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0437 - root_mean_squared_error: 0.2091 - val_loss: 0.4245 - val_root_mean_squared_error: 0.6515\n",
      "Epoch 96/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0424 - root_mean_squared_error: 0.2058 - val_loss: 0.3939 - val_root_mean_squared_error: 0.6276\n",
      "Epoch 97/180\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0416 - root_mean_squared_error: 0.2041 - val_loss: 0.3704 - val_root_mean_squared_error: 0.6086\n",
      "Epoch 98/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0406 - root_mean_squared_error: 0.2014 - val_loss: 0.3210 - val_root_mean_squared_error: 0.5666\n",
      "Epoch 99/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0399 - root_mean_squared_error: 0.1998 - val_loss: 0.3162 - val_root_mean_squared_error: 0.5623\n",
      "Epoch 100/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0386 - root_mean_squared_error: 0.1965 - val_loss: 0.2742 - val_root_mean_squared_error: 0.5237\n",
      "Epoch 101/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0376 - root_mean_squared_error: 0.1939 - val_loss: 0.2467 - val_root_mean_squared_error: 0.4966\n",
      "Epoch 102/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0365 - root_mean_squared_error: 0.1911 - val_loss: 0.2716 - val_root_mean_squared_error: 0.5212\n",
      "Epoch 103/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0357 - root_mean_squared_error: 0.1891 - val_loss: 0.2620 - val_root_mean_squared_error: 0.5118\n",
      "Epoch 104/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0349 - root_mean_squared_error: 0.1867 - val_loss: 0.2557 - val_root_mean_squared_error: 0.5056\n",
      "Epoch 105/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0343 - root_mean_squared_error: 0.1851 - val_loss: 0.2525 - val_root_mean_squared_error: 0.5025\n",
      "Epoch 106/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0334 - root_mean_squared_error: 0.1829 - val_loss: 0.2252 - val_root_mean_squared_error: 0.4746\n",
      "Epoch 107/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0330 - root_mean_squared_error: 0.1817 - val_loss: 0.2046 - val_root_mean_squared_error: 0.4523\n",
      "Epoch 108/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0325 - root_mean_squared_error: 0.1802 - val_loss: 0.2144 - val_root_mean_squared_error: 0.4631\n",
      "Epoch 109/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0317 - root_mean_squared_error: 0.1781 - val_loss: 0.2041 - val_root_mean_squared_error: 0.4517\n",
      "Epoch 110/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0314 - root_mean_squared_error: 0.1772 - val_loss: 0.1911 - val_root_mean_squared_error: 0.4372\n",
      "Epoch 111/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0304 - root_mean_squared_error: 0.1745 - val_loss: 0.1819 - val_root_mean_squared_error: 0.4265\n",
      "Epoch 112/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0299 - root_mean_squared_error: 0.1728 - val_loss: 0.1931 - val_root_mean_squared_error: 0.4394\n",
      "Epoch 113/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0292 - root_mean_squared_error: 0.1709 - val_loss: 0.1808 - val_root_mean_squared_error: 0.4252\n",
      "Epoch 114/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0286 - root_mean_squared_error: 0.1692 - val_loss: 0.1702 - val_root_mean_squared_error: 0.4126\n",
      "Epoch 115/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0281 - root_mean_squared_error: 0.1675 - val_loss: 0.1677 - val_root_mean_squared_error: 0.4095\n",
      "Epoch 116/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0278 - root_mean_squared_error: 0.1668 - val_loss: 0.1703 - val_root_mean_squared_error: 0.4127\n",
      "Epoch 117/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0274 - root_mean_squared_error: 0.1655 - val_loss: 0.1626 - val_root_mean_squared_error: 0.4032\n",
      "Epoch 118/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0265 - root_mean_squared_error: 0.1629 - val_loss: 0.1598 - val_root_mean_squared_error: 0.3997\n",
      "Epoch 119/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0261 - root_mean_squared_error: 0.1616 - val_loss: 0.1665 - val_root_mean_squared_error: 0.4081\n",
      "Epoch 120/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0255 - root_mean_squared_error: 0.1596 - val_loss: 0.1507 - val_root_mean_squared_error: 0.3882\n",
      "Epoch 121/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0251 - root_mean_squared_error: 0.1583 - val_loss: 0.1390 - val_root_mean_squared_error: 0.3728\n",
      "Epoch 122/180\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0250 - root_mean_squared_error: 0.1580 - val_loss: 0.1432 - val_root_mean_squared_error: 0.3784\n",
      "Epoch 123/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0243 - root_mean_squared_error: 0.1558 - val_loss: 0.1366 - val_root_mean_squared_error: 0.3696\n",
      "Epoch 124/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0240 - root_mean_squared_error: 0.1550 - val_loss: 0.1452 - val_root_mean_squared_error: 0.3811\n",
      "Epoch 125/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0234 - root_mean_squared_error: 0.1531 - val_loss: 0.1055 - val_root_mean_squared_error: 0.3248\n",
      "Epoch 126/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0231 - root_mean_squared_error: 0.1520 - val_loss: 0.1056 - val_root_mean_squared_error: 0.3250\n",
      "Epoch 127/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0232 - root_mean_squared_error: 0.1522 - val_loss: 0.1090 - val_root_mean_squared_error: 0.3302\n",
      "Epoch 128/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0223 - root_mean_squared_error: 0.1492 - val_loss: 0.1004 - val_root_mean_squared_error: 0.3169\n",
      "Epoch 129/180\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0218 - root_mean_squared_error: 0.1477 - val_loss: 0.0994 - val_root_mean_squared_error: 0.3152\n",
      "Epoch 130/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0216 - root_mean_squared_error: 0.1469 - val_loss: 0.1047 - val_root_mean_squared_error: 0.3235\n",
      "Epoch 131/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0211 - root_mean_squared_error: 0.1452 - val_loss: 0.1060 - val_root_mean_squared_error: 0.3256\n",
      "Epoch 132/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0206 - root_mean_squared_error: 0.1435 - val_loss: 0.1118 - val_root_mean_squared_error: 0.3344\n",
      "Epoch 133/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0203 - root_mean_squared_error: 0.1424 - val_loss: 0.1157 - val_root_mean_squared_error: 0.3401\n",
      "Epoch 134/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0198 - root_mean_squared_error: 0.1409 - val_loss: 0.1144 - val_root_mean_squared_error: 0.3382\n",
      "Epoch 135/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0196 - root_mean_squared_error: 0.1399 - val_loss: 0.1183 - val_root_mean_squared_error: 0.3439\n",
      "Epoch 136/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0194 - root_mean_squared_error: 0.1394 - val_loss: 0.1138 - val_root_mean_squared_error: 0.3373\n",
      "Epoch 137/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0188 - root_mean_squared_error: 0.1372 - val_loss: 0.1045 - val_root_mean_squared_error: 0.3232\n",
      "Epoch 138/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0187 - root_mean_squared_error: 0.1368 - val_loss: 0.1067 - val_root_mean_squared_error: 0.3267\n",
      "Epoch 139/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0183 - root_mean_squared_error: 0.1351 - val_loss: 0.0920 - val_root_mean_squared_error: 0.3033\n",
      "Epoch 140/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0180 - root_mean_squared_error: 0.1341 - val_loss: 0.0939 - val_root_mean_squared_error: 0.3064\n",
      "Epoch 141/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0176 - root_mean_squared_error: 0.1328 - val_loss: 0.0908 - val_root_mean_squared_error: 0.3013\n",
      "Epoch 142/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0173 - root_mean_squared_error: 0.1316 - val_loss: 0.0945 - val_root_mean_squared_error: 0.3074\n",
      "Epoch 143/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0171 - root_mean_squared_error: 0.1308 - val_loss: 0.0985 - val_root_mean_squared_error: 0.3139\n",
      "Epoch 144/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0166 - root_mean_squared_error: 0.1289 - val_loss: 0.0965 - val_root_mean_squared_error: 0.3106\n",
      "Epoch 145/180\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0165 - root_mean_squared_error: 0.1285 - val_loss: 0.0940 - val_root_mean_squared_error: 0.3066\n",
      "Epoch 146/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0162 - root_mean_squared_error: 0.1273 - val_loss: 0.1081 - val_root_mean_squared_error: 0.3287\n",
      "Epoch 147/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0159 - root_mean_squared_error: 0.1261 - val_loss: 0.1119 - val_root_mean_squared_error: 0.3344\n",
      "Epoch 148/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0157 - root_mean_squared_error: 0.1254 - val_loss: 0.1102 - val_root_mean_squared_error: 0.3320\n",
      "Epoch 149/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0156 - root_mean_squared_error: 0.1250 - val_loss: 0.0987 - val_root_mean_squared_error: 0.3141\n",
      "Epoch 150/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0152 - root_mean_squared_error: 0.1232 - val_loss: 0.1062 - val_root_mean_squared_error: 0.3258\n",
      "Epoch 151/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0150 - root_mean_squared_error: 0.1224 - val_loss: 0.1005 - val_root_mean_squared_error: 0.3170\n",
      "Epoch 152/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0151 - root_mean_squared_error: 0.1229 - val_loss: 0.0975 - val_root_mean_squared_error: 0.3123\n",
      "Epoch 153/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0147 - root_mean_squared_error: 0.1214 - val_loss: 0.1006 - val_root_mean_squared_error: 0.3172\n",
      "Epoch 154/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0144 - root_mean_squared_error: 0.1200 - val_loss: 0.0914 - val_root_mean_squared_error: 0.3024\n",
      "Epoch 155/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0141 - root_mean_squared_error: 0.1187 - val_loss: 0.0905 - val_root_mean_squared_error: 0.3008\n",
      "Epoch 156/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0140 - root_mean_squared_error: 0.1184 - val_loss: 0.0890 - val_root_mean_squared_error: 0.2984\n",
      "Epoch 157/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0136 - root_mean_squared_error: 0.1166 - val_loss: 0.0930 - val_root_mean_squared_error: 0.3049\n",
      "Epoch 158/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0135 - root_mean_squared_error: 0.1163 - val_loss: 0.0897 - val_root_mean_squared_error: 0.2995\n",
      "Epoch 159/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0134 - root_mean_squared_error: 0.1156 - val_loss: 0.0827 - val_root_mean_squared_error: 0.2875\n",
      "Epoch 160/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0130 - root_mean_squared_error: 0.1141 - val_loss: 0.0844 - val_root_mean_squared_error: 0.2906\n",
      "Epoch 161/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0130 - root_mean_squared_error: 0.1139 - val_loss: 0.0812 - val_root_mean_squared_error: 0.2849\n",
      "Epoch 162/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0128 - root_mean_squared_error: 0.1133 - val_loss: 0.0838 - val_root_mean_squared_error: 0.2894\n",
      "Epoch 163/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0132 - root_mean_squared_error: 0.1147 - val_loss: 0.1225 - val_root_mean_squared_error: 0.3501\n",
      "Epoch 164/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0126 - root_mean_squared_error: 0.1121 - val_loss: 0.1172 - val_root_mean_squared_error: 0.3423\n",
      "Epoch 165/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0123 - root_mean_squared_error: 0.1110 - val_loss: 0.1181 - val_root_mean_squared_error: 0.3436\n",
      "Epoch 166/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0120 - root_mean_squared_error: 0.1095 - val_loss: 0.1136 - val_root_mean_squared_error: 0.3371\n",
      "Epoch 167/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0119 - root_mean_squared_error: 0.1089 - val_loss: 0.0992 - val_root_mean_squared_error: 0.3150\n",
      "Epoch 168/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0116 - root_mean_squared_error: 0.1077 - val_loss: 0.0979 - val_root_mean_squared_error: 0.3130\n",
      "Epoch 169/180\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0116 - root_mean_squared_error: 0.1075 - val_loss: 0.0950 - val_root_mean_squared_error: 0.3082\n",
      "Epoch 170/180\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0112 - root_mean_squared_error: 0.1057 - val_loss: 0.0841 - val_root_mean_squared_error: 0.2900\n",
      "Epoch 171/180\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0111 - root_mean_squared_error: 0.1053 - val_loss: 0.0864 - val_root_mean_squared_error: 0.2939\n",
      "Epoch 172/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0110 - root_mean_squared_error: 0.1050 - val_loss: 0.0835 - val_root_mean_squared_error: 0.2889\n",
      "Epoch 173/180\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0109 - root_mean_squared_error: 0.1045 - val_loss: 0.0758 - val_root_mean_squared_error: 0.2754\n",
      "Epoch 174/180\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0106 - root_mean_squared_error: 0.1028 - val_loss: 0.0800 - val_root_mean_squared_error: 0.2829\n",
      "Epoch 175/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0105 - root_mean_squared_error: 0.1025 - val_loss: 0.0694 - val_root_mean_squared_error: 0.2634\n",
      "Epoch 176/180\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0103 - root_mean_squared_error: 0.1015 - val_loss: 0.0657 - val_root_mean_squared_error: 0.2564\n",
      "Epoch 177/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0101 - root_mean_squared_error: 0.1007 - val_loss: 0.0644 - val_root_mean_squared_error: 0.2537\n",
      "Epoch 178/180\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0101 - root_mean_squared_error: 0.1004 - val_loss: 0.0649 - val_root_mean_squared_error: 0.2547\n",
      "Epoch 179/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0098 - root_mean_squared_error: 0.0991 - val_loss: 0.0639 - val_root_mean_squared_error: 0.2528\n",
      "Epoch 180/180\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0098 - root_mean_squared_error: 0.0988 - val_loss: 0.0632 - val_root_mean_squared_error: 0.2513\n"
     ]
    }
   ],
   "source": [
    "# BiLSTM before\n",
    "model_before_BiLSTM = Sequential()\n",
    "model_before_BiLSTM.add(InputLayer((7, 8)))\n",
    "model_before_BiLSTM.add(Bidirectional(LSTM(32, activation='relu', input_shape=(X2_before_train.shape[1], X2_before_train.shape[2]),return_sequences=False)))\n",
    "\n",
    "# model_BiLSTM.add(Dense(8, 'relu'))\n",
    "# model_BiLSTM.add(Dropout(0.2))\n",
    "model_before_BiLSTM.add(Dense(1, 'linear'))\n",
    "\n",
    "# model_Multivariate.build(input_shape=(None, 64, 8, 1))\n",
    "# model_Multivariate.summary()\n",
    "\n",
    "# cp_lstm = ModelCheckpoint('model_lstm/', save_best_only=True)\n",
    "model_before_BiLSTM.compile(loss=MeanSquaredError(), optimizer=Adam(learning_rate=0.0001), metrics=[RootMeanSquaredError()])\n",
    "history = model_before_BiLSTM.fit(X2_before_train, y2_before_train, batch_size=32, validation_data=(X2_before_val, y2_before_val), epochs=180)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1598,
   "id": "5f78c0f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/120\n",
      "9/9 [==============================] - 0s 7ms/step - loss: 346.3224 - root_mean_squared_error: 18.6097 - val_loss: 470.9933 - val_root_mean_squared_error: 21.7024\n",
      "Epoch 2/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 316.4705 - root_mean_squared_error: 17.7896 - val_loss: 430.9177 - val_root_mean_squared_error: 20.7586\n",
      "Epoch 3/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 288.4297 - root_mean_squared_error: 16.9832 - val_loss: 393.5981 - val_root_mean_squared_error: 19.8393\n",
      "Epoch 4/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 262.0767 - root_mean_squared_error: 16.1888 - val_loss: 358.6720 - val_root_mean_squared_error: 18.9386\n",
      "Epoch 5/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 237.7071 - root_mean_squared_error: 15.4178 - val_loss: 326.2587 - val_root_mean_squared_error: 18.0626\n",
      "Epoch 6/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 215.0207 - root_mean_squared_error: 14.6636 - val_loss: 295.9240 - val_root_mean_squared_error: 17.2024\n",
      "Epoch 7/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 193.9459 - root_mean_squared_error: 13.9264 - val_loss: 267.7003 - val_root_mean_squared_error: 16.3616\n",
      "Epoch 8/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 174.5437 - root_mean_squared_error: 13.2115 - val_loss: 241.7175 - val_root_mean_squared_error: 15.5473\n",
      "Epoch 9/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 156.4149 - root_mean_squared_error: 12.5066 - val_loss: 217.5477 - val_root_mean_squared_error: 14.7495\n",
      "Epoch 10/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 139.8244 - root_mean_squared_error: 11.8247 - val_loss: 195.3663 - val_root_mean_squared_error: 13.9774\n",
      "Epoch 11/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 124.5430 - root_mean_squared_error: 11.1599 - val_loss: 174.9480 - val_root_mean_squared_error: 13.2268\n",
      "Epoch 12/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 110.4279 - root_mean_squared_error: 10.5085 - val_loss: 155.7982 - val_root_mean_squared_error: 12.4819\n",
      "Epoch 13/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 97.4596 - root_mean_squared_error: 9.8722 - val_loss: 137.9854 - val_root_mean_squared_error: 11.7467\n",
      "Epoch 14/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 85.4715 - root_mean_squared_error: 9.2451 - val_loss: 121.5889 - val_root_mean_squared_error: 11.0267\n",
      "Epoch 15/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 74.4100 - root_mean_squared_error: 8.6261 - val_loss: 106.9137 - val_root_mean_squared_error: 10.3399\n",
      "Epoch 16/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 64.6765 - root_mean_squared_error: 8.0422 - val_loss: 93.5182 - val_root_mean_squared_error: 9.6705\n",
      "Epoch 17/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 55.8783 - root_mean_squared_error: 7.4752 - val_loss: 81.4682 - val_root_mean_squared_error: 9.0260\n",
      "Epoch 18/120\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 47.9186 - root_mean_squared_error: 6.9223 - val_loss: 70.6095 - val_root_mean_squared_error: 8.4029\n",
      "Epoch 19/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 40.8766 - root_mean_squared_error: 6.3935 - val_loss: 60.8806 - val_root_mean_squared_error: 7.8026\n",
      "Epoch 20/120\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 34.6087 - root_mean_squared_error: 5.8829 - val_loss: 52.2578 - val_root_mean_squared_error: 7.2290\n",
      "Epoch 21/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 29.1971 - root_mean_squared_error: 5.4034 - val_loss: 44.6743 - val_root_mean_squared_error: 6.6839\n",
      "Epoch 22/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 24.4440 - root_mean_squared_error: 4.9441 - val_loss: 37.9475 - val_root_mean_squared_error: 6.1602\n",
      "Epoch 23/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 20.3113 - root_mean_squared_error: 4.5068 - val_loss: 32.0066 - val_root_mean_squared_error: 5.6574\n",
      "Epoch 24/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 16.7052 - root_mean_squared_error: 4.0872 - val_loss: 26.9463 - val_root_mean_squared_error: 5.1910\n",
      "Epoch 25/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 13.7480 - root_mean_squared_error: 3.7078 - val_loss: 22.5800 - val_root_mean_squared_error: 4.7518\n",
      "Epoch 26/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 11.2048 - root_mean_squared_error: 3.3474 - val_loss: 18.8853 - val_root_mean_squared_error: 4.3457\n",
      "Epoch 27/120\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 9.1313 - root_mean_squared_error: 3.0218 - val_loss: 15.7550 - val_root_mean_squared_error: 3.9693\n",
      "Epoch 28/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 7.4032 - root_mean_squared_error: 2.7209 - val_loss: 13.1678 - val_root_mean_squared_error: 3.6288\n",
      "Epoch 29/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 5.9912 - root_mean_squared_error: 2.4477 - val_loss: 10.9565 - val_root_mean_squared_error: 3.3101\n",
      "Epoch 30/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 4.8597 - root_mean_squared_error: 2.2045 - val_loss: 9.1568 - val_root_mean_squared_error: 3.0260\n",
      "Epoch 31/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 3.9514 - root_mean_squared_error: 1.9878 - val_loss: 7.6116 - val_root_mean_squared_error: 2.7589\n",
      "Epoch 32/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 3.1982 - root_mean_squared_error: 1.7884 - val_loss: 6.3103 - val_root_mean_squared_error: 2.5120\n",
      "Epoch 33/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 2.6089 - root_mean_squared_error: 1.6152 - val_loss: 5.2784 - val_root_mean_squared_error: 2.2975\n",
      "Epoch 34/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 2.1509 - root_mean_squared_error: 1.4666 - val_loss: 4.4204 - val_root_mean_squared_error: 2.1025\n",
      "Epoch 35/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 1.7929 - root_mean_squared_error: 1.3390 - val_loss: 3.7484 - val_root_mean_squared_error: 1.9361\n",
      "Epoch 36/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 1.5231 - root_mean_squared_error: 1.2341 - val_loss: 3.1947 - val_root_mean_squared_error: 1.7874\n",
      "Epoch 37/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 1.3181 - root_mean_squared_error: 1.1481 - val_loss: 2.7488 - val_root_mean_squared_error: 1.6580\n",
      "Epoch 38/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 1.1559 - root_mean_squared_error: 1.0751 - val_loss: 2.3858 - val_root_mean_squared_error: 1.5446\n",
      "Epoch 39/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 1.0285 - root_mean_squared_error: 1.0141 - val_loss: 2.0753 - val_root_mean_squared_error: 1.4406\n",
      "Epoch 40/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.9309 - root_mean_squared_error: 0.9648 - val_loss: 1.8350 - val_root_mean_squared_error: 1.3546\n",
      "Epoch 41/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.8527 - root_mean_squared_error: 0.9234 - val_loss: 1.6342 - val_root_mean_squared_error: 1.2784\n",
      "Epoch 42/120\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.7929 - root_mean_squared_error: 0.8904 - val_loss: 1.4617 - val_root_mean_squared_error: 1.2090\n",
      "Epoch 43/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.7462 - root_mean_squared_error: 0.8638 - val_loss: 1.3173 - val_root_mean_squared_error: 1.1477\n",
      "Epoch 44/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.7078 - root_mean_squared_error: 0.8413 - val_loss: 1.1984 - val_root_mean_squared_error: 1.0947\n",
      "Epoch 45/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.6757 - root_mean_squared_error: 0.8220 - val_loss: 1.0914 - val_root_mean_squared_error: 1.0447\n",
      "Epoch 46/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.6495 - root_mean_squared_error: 0.8059 - val_loss: 1.0002 - val_root_mean_squared_error: 1.0001\n",
      "Epoch 47/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.6254 - root_mean_squared_error: 0.7908 - val_loss: 0.9395 - val_root_mean_squared_error: 0.9693\n",
      "Epoch 48/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.6078 - root_mean_squared_error: 0.7796 - val_loss: 0.8882 - val_root_mean_squared_error: 0.9424\n",
      "Epoch 49/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.5920 - root_mean_squared_error: 0.7694 - val_loss: 0.8533 - val_root_mean_squared_error: 0.9238\n",
      "Epoch 50/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.5769 - root_mean_squared_error: 0.7596 - val_loss: 0.8148 - val_root_mean_squared_error: 0.9027\n",
      "Epoch 51/120\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.5624 - root_mean_squared_error: 0.7499 - val_loss: 0.7701 - val_root_mean_squared_error: 0.8776\n",
      "Epoch 52/120\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.5496 - root_mean_squared_error: 0.7414 - val_loss: 0.7293 - val_root_mean_squared_error: 0.8540\n",
      "Epoch 53/120\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.5378 - root_mean_squared_error: 0.7334 - val_loss: 0.6901 - val_root_mean_squared_error: 0.8307\n",
      "Epoch 54/120\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.5274 - root_mean_squared_error: 0.7263 - val_loss: 0.6610 - val_root_mean_squared_error: 0.8130\n",
      "Epoch 55/120\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.5170 - root_mean_squared_error: 0.7190 - val_loss: 0.6414 - val_root_mean_squared_error: 0.8009\n",
      "Epoch 56/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.5077 - root_mean_squared_error: 0.7125 - val_loss: 0.6252 - val_root_mean_squared_error: 0.7907\n",
      "Epoch 57/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.4990 - root_mean_squared_error: 0.7064 - val_loss: 0.6227 - val_root_mean_squared_error: 0.7891\n",
      "Epoch 58/120\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.4883 - root_mean_squared_error: 0.6988 - val_loss: 0.6076 - val_root_mean_squared_error: 0.7795\n",
      "Epoch 59/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.4781 - root_mean_squared_error: 0.6915 - val_loss: 0.5987 - val_root_mean_squared_error: 0.7738\n",
      "Epoch 60/120\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.4692 - root_mean_squared_error: 0.6850 - val_loss: 0.5942 - val_root_mean_squared_error: 0.7708\n",
      "Epoch 61/120\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.4620 - root_mean_squared_error: 0.6797 - val_loss: 0.5838 - val_root_mean_squared_error: 0.7640\n",
      "Epoch 62/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.4551 - root_mean_squared_error: 0.6746 - val_loss: 0.5797 - val_root_mean_squared_error: 0.7614\n",
      "Epoch 63/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.4478 - root_mean_squared_error: 0.6692 - val_loss: 0.5644 - val_root_mean_squared_error: 0.7513\n",
      "Epoch 64/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.4415 - root_mean_squared_error: 0.6644 - val_loss: 0.5575 - val_root_mean_squared_error: 0.7466\n",
      "Epoch 65/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.4358 - root_mean_squared_error: 0.6601 - val_loss: 0.5486 - val_root_mean_squared_error: 0.7407\n",
      "Epoch 66/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.4296 - root_mean_squared_error: 0.6554 - val_loss: 0.5392 - val_root_mean_squared_error: 0.7343\n",
      "Epoch 67/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.4239 - root_mean_squared_error: 0.6511 - val_loss: 0.5342 - val_root_mean_squared_error: 0.7309\n",
      "Epoch 68/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.4188 - root_mean_squared_error: 0.6472 - val_loss: 0.5219 - val_root_mean_squared_error: 0.7224\n",
      "Epoch 69/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.4134 - root_mean_squared_error: 0.6430 - val_loss: 0.5087 - val_root_mean_squared_error: 0.7132\n",
      "Epoch 70/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.4080 - root_mean_squared_error: 0.6387 - val_loss: 0.5022 - val_root_mean_squared_error: 0.7087\n",
      "Epoch 71/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.4032 - root_mean_squared_error: 0.6350 - val_loss: 0.4994 - val_root_mean_squared_error: 0.7067\n",
      "Epoch 72/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.3986 - root_mean_squared_error: 0.6313 - val_loss: 0.4905 - val_root_mean_squared_error: 0.7004\n",
      "Epoch 73/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.3936 - root_mean_squared_error: 0.6274 - val_loss: 0.4991 - val_root_mean_squared_error: 0.7065\n",
      "Epoch 74/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.3887 - root_mean_squared_error: 0.6235 - val_loss: 0.4961 - val_root_mean_squared_error: 0.7044\n",
      "Epoch 75/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.3847 - root_mean_squared_error: 0.6203 - val_loss: 0.4852 - val_root_mean_squared_error: 0.6966\n",
      "Epoch 76/120\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.3807 - root_mean_squared_error: 0.6170 - val_loss: 0.4685 - val_root_mean_squared_error: 0.6845\n",
      "Epoch 77/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.3771 - root_mean_squared_error: 0.6141 - val_loss: 0.4606 - val_root_mean_squared_error: 0.6787\n",
      "Epoch 78/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.3738 - root_mean_squared_error: 0.6114 - val_loss: 0.4572 - val_root_mean_squared_error: 0.6762\n",
      "Epoch 79/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.3704 - root_mean_squared_error: 0.6086 - val_loss: 0.4577 - val_root_mean_squared_error: 0.6766\n",
      "Epoch 80/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.3670 - root_mean_squared_error: 0.6058 - val_loss: 0.4540 - val_root_mean_squared_error: 0.6738\n",
      "Epoch 81/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.3645 - root_mean_squared_error: 0.6037 - val_loss: 0.4447 - val_root_mean_squared_error: 0.6668\n",
      "Epoch 82/120\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.3616 - root_mean_squared_error: 0.6014 - val_loss: 0.4359 - val_root_mean_squared_error: 0.6602\n",
      "Epoch 83/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.3589 - root_mean_squared_error: 0.5991 - val_loss: 0.4295 - val_root_mean_squared_error: 0.6554\n",
      "Epoch 84/120\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.3563 - root_mean_squared_error: 0.5969 - val_loss: 0.4269 - val_root_mean_squared_error: 0.6533\n",
      "Epoch 85/120\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.3540 - root_mean_squared_error: 0.5950 - val_loss: 0.4203 - val_root_mean_squared_error: 0.6483\n",
      "Epoch 86/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.3519 - root_mean_squared_error: 0.5932 - val_loss: 0.4028 - val_root_mean_squared_error: 0.6347\n",
      "Epoch 87/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.3494 - root_mean_squared_error: 0.5911 - val_loss: 0.4011 - val_root_mean_squared_error: 0.6333\n",
      "Epoch 88/120\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.3473 - root_mean_squared_error: 0.5893 - val_loss: 0.3967 - val_root_mean_squared_error: 0.6298\n",
      "Epoch 89/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.3454 - root_mean_squared_error: 0.5877 - val_loss: 0.3934 - val_root_mean_squared_error: 0.6272\n",
      "Epoch 90/120\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.3436 - root_mean_squared_error: 0.5862 - val_loss: 0.3878 - val_root_mean_squared_error: 0.6227\n",
      "Epoch 91/120\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.3417 - root_mean_squared_error: 0.5846 - val_loss: 0.3834 - val_root_mean_squared_error: 0.6192\n",
      "Epoch 92/120\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.3401 - root_mean_squared_error: 0.5832 - val_loss: 0.3762 - val_root_mean_squared_error: 0.6134\n",
      "Epoch 93/120\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.3386 - root_mean_squared_error: 0.5819 - val_loss: 0.3744 - val_root_mean_squared_error: 0.6118\n",
      "Epoch 94/120\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.3369 - root_mean_squared_error: 0.5804 - val_loss: 0.3737 - val_root_mean_squared_error: 0.6113\n",
      "Epoch 95/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.3357 - root_mean_squared_error: 0.5794 - val_loss: 0.3807 - val_root_mean_squared_error: 0.6170\n",
      "Epoch 96/120\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 0s 1ms/step - loss: 0.3340 - root_mean_squared_error: 0.5779 - val_loss: 0.3810 - val_root_mean_squared_error: 0.6173\n",
      "Epoch 97/120\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.3326 - root_mean_squared_error: 0.5767 - val_loss: 0.3920 - val_root_mean_squared_error: 0.6261\n",
      "Epoch 98/120\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.3310 - root_mean_squared_error: 0.5753 - val_loss: 0.3850 - val_root_mean_squared_error: 0.6205\n",
      "Epoch 99/120\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.3295 - root_mean_squared_error: 0.5740 - val_loss: 0.3742 - val_root_mean_squared_error: 0.6117\n",
      "Epoch 100/120\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.3280 - root_mean_squared_error: 0.5727 - val_loss: 0.3706 - val_root_mean_squared_error: 0.6088\n",
      "Epoch 101/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.3267 - root_mean_squared_error: 0.5716 - val_loss: 0.3651 - val_root_mean_squared_error: 0.6043\n",
      "Epoch 102/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.3254 - root_mean_squared_error: 0.5704 - val_loss: 0.3537 - val_root_mean_squared_error: 0.5947\n",
      "Epoch 103/120\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.3242 - root_mean_squared_error: 0.5693 - val_loss: 0.3525 - val_root_mean_squared_error: 0.5937\n",
      "Epoch 104/120\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.3225 - root_mean_squared_error: 0.5679 - val_loss: 0.3603 - val_root_mean_squared_error: 0.6002\n",
      "Epoch 105/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.3215 - root_mean_squared_error: 0.5670 - val_loss: 0.3604 - val_root_mean_squared_error: 0.6004\n",
      "Epoch 106/120\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.3203 - root_mean_squared_error: 0.5660 - val_loss: 0.3597 - val_root_mean_squared_error: 0.5998\n",
      "Epoch 107/120\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.3192 - root_mean_squared_error: 0.5649 - val_loss: 0.3578 - val_root_mean_squared_error: 0.5981\n",
      "Epoch 108/120\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.3182 - root_mean_squared_error: 0.5641 - val_loss: 0.3553 - val_root_mean_squared_error: 0.5961\n",
      "Epoch 109/120\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.3173 - root_mean_squared_error: 0.5633 - val_loss: 0.3473 - val_root_mean_squared_error: 0.5893\n",
      "Epoch 110/120\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.3160 - root_mean_squared_error: 0.5621 - val_loss: 0.3403 - val_root_mean_squared_error: 0.5833\n",
      "Epoch 111/120\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.3152 - root_mean_squared_error: 0.5614 - val_loss: 0.3308 - val_root_mean_squared_error: 0.5752\n",
      "Epoch 112/120\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.3142 - root_mean_squared_error: 0.5605 - val_loss: 0.3335 - val_root_mean_squared_error: 0.5775\n",
      "Epoch 113/120\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.3132 - root_mean_squared_error: 0.5596 - val_loss: 0.3335 - val_root_mean_squared_error: 0.5775\n",
      "Epoch 114/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.3122 - root_mean_squared_error: 0.5588 - val_loss: 0.3325 - val_root_mean_squared_error: 0.5766\n",
      "Epoch 115/120\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.3111 - root_mean_squared_error: 0.5577 - val_loss: 0.3303 - val_root_mean_squared_error: 0.5747\n",
      "Epoch 116/120\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.3100 - root_mean_squared_error: 0.5567 - val_loss: 0.3324 - val_root_mean_squared_error: 0.5765\n",
      "Epoch 117/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.3089 - root_mean_squared_error: 0.5558 - val_loss: 0.3310 - val_root_mean_squared_error: 0.5753\n",
      "Epoch 118/120\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.3081 - root_mean_squared_error: 0.5551 - val_loss: 0.3303 - val_root_mean_squared_error: 0.5747\n",
      "Epoch 119/120\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.3071 - root_mean_squared_error: 0.5542 - val_loss: 0.3279 - val_root_mean_squared_error: 0.5726\n",
      "Epoch 120/120\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.3056 - root_mean_squared_error: 0.5528 - val_loss: 0.3131 - val_root_mean_squared_error: 0.5595\n"
     ]
    }
   ],
   "source": [
    "# CNN\n",
    "model_cnn = Sequential()\n",
    "model_cnn.add(InputLayer((7, 8)))\n",
    "model_cnn.add(Conv1D(32, kernel_size=2))\n",
    "model_cnn.add(Flatten())\n",
    "# model_cnn.add(Dropout(0.1))\n",
    "# model_cnn.add(Dense(8, 'relu'))\n",
    "\n",
    "model_cnn.add(Dense(1, 'linear'))\n",
    "\n",
    "# model_cnn.summary()\n",
    "model_cnn.compile(loss=MeanSquaredError(), optimizer=Adam(learning_rate=0.0001), metrics=[RootMeanSquaredError()])\n",
    "history = model_cnn.fit(X2_train, y2_train, batch_size=32, validation_data=(X2_val, y2_val), epochs=120)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1368,
   "id": "a68e5a45",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "9/9 [==============================] - 0s 6ms/step - loss: 80.8470 - root_mean_squared_error: 8.9915 - val_loss: 48.9409 - val_root_mean_squared_error: 6.9958\n",
      "Epoch 2/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 73.0989 - root_mean_squared_error: 8.5498 - val_loss: 44.9361 - val_root_mean_squared_error: 6.7034\n",
      "Epoch 3/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 65.7818 - root_mean_squared_error: 8.1106 - val_loss: 41.2840 - val_root_mean_squared_error: 6.4253\n",
      "Epoch 4/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 59.1728 - root_mean_squared_error: 7.6924 - val_loss: 37.8046 - val_root_mean_squared_error: 6.1485\n",
      "Epoch 5/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 52.9603 - root_mean_squared_error: 7.2774 - val_loss: 34.6041 - val_root_mean_squared_error: 5.8825\n",
      "Epoch 6/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 47.3443 - root_mean_squared_error: 6.8807 - val_loss: 31.6523 - val_root_mean_squared_error: 5.6260\n",
      "Epoch 7/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 42.1811 - root_mean_squared_error: 6.4947 - val_loss: 28.9135 - val_root_mean_squared_error: 5.3771\n",
      "Epoch 8/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 37.4060 - root_mean_squared_error: 6.1160 - val_loss: 26.4185 - val_root_mean_squared_error: 5.1399\n",
      "Epoch 9/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 33.0686 - root_mean_squared_error: 5.7505 - val_loss: 23.9872 - val_root_mean_squared_error: 4.8977\n",
      "Epoch 10/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 29.0559 - root_mean_squared_error: 5.3904 - val_loss: 21.7516 - val_root_mean_squared_error: 4.6639\n",
      "Epoch 11/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 25.4376 - root_mean_squared_error: 5.0436 - val_loss: 19.7012 - val_root_mean_squared_error: 4.4386\n",
      "Epoch 12/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 22.1934 - root_mean_squared_error: 4.7110 - val_loss: 17.8508 - val_root_mean_squared_error: 4.2250\n",
      "Epoch 13/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 19.3031 - root_mean_squared_error: 4.3935 - val_loss: 16.1538 - val_root_mean_squared_error: 4.0192\n",
      "Epoch 14/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 16.6911 - root_mean_squared_error: 4.0855 - val_loss: 14.6228 - val_root_mean_squared_error: 3.8240\n",
      "Epoch 15/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 14.3482 - root_mean_squared_error: 3.7879 - val_loss: 13.1942 - val_root_mean_squared_error: 3.6324\n",
      "Epoch 16/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 12.2530 - root_mean_squared_error: 3.5004 - val_loss: 11.8972 - val_root_mean_squared_error: 3.4492\n",
      "Epoch 17/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 10.4118 - root_mean_squared_error: 3.2267 - val_loss: 10.7238 - val_root_mean_squared_error: 3.2747\n",
      "Epoch 18/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 8.7966 - root_mean_squared_error: 2.9659 - val_loss: 9.6675 - val_root_mean_squared_error: 3.1093\n",
      "Epoch 19/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 7.3952 - root_mean_squared_error: 2.7194 - val_loss: 8.7103 - val_root_mean_squared_error: 2.9513\n",
      "Epoch 20/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 6.1801 - root_mean_squared_error: 2.4860 - val_loss: 7.8501 - val_root_mean_squared_error: 2.8018\n",
      "Epoch 21/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 5.1180 - root_mean_squared_error: 2.2623 - val_loss: 7.0798 - val_root_mean_squared_error: 2.6608\n",
      "Epoch 22/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 4.2236 - root_mean_squared_error: 2.0551 - val_loss: 6.3903 - val_root_mean_squared_error: 2.5279\n",
      "Epoch 23/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 3.4707 - root_mean_squared_error: 1.8630 - val_loss: 5.7863 - val_root_mean_squared_error: 2.4055\n",
      "Epoch 24/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 2.8458 - root_mean_squared_error: 1.6869 - val_loss: 5.2462 - val_root_mean_squared_error: 2.2905\n",
      "Epoch 25/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 2.3318 - root_mean_squared_error: 1.5270 - val_loss: 4.7654 - val_root_mean_squared_error: 2.1830\n",
      "Epoch 26/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 1.8961 - root_mean_squared_error: 1.3770 - val_loss: 4.3384 - val_root_mean_squared_error: 2.0829\n",
      "Epoch 27/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 1.5546 - root_mean_squared_error: 1.2468 - val_loss: 3.9681 - val_root_mean_squared_error: 1.9920\n",
      "Epoch 28/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 1.2829 - root_mean_squared_error: 1.1326 - val_loss: 3.6447 - val_root_mean_squared_error: 1.9091\n",
      "Epoch 29/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 1.0681 - root_mean_squared_error: 1.0335 - val_loss: 3.3695 - val_root_mean_squared_error: 1.8356\n",
      "Epoch 30/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.8966 - root_mean_squared_error: 0.9469 - val_loss: 3.1266 - val_root_mean_squared_error: 1.7682\n",
      "Epoch 31/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.7652 - root_mean_squared_error: 0.8747 - val_loss: 2.9136 - val_root_mean_squared_error: 1.7069\n",
      "Epoch 32/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.6622 - root_mean_squared_error: 0.8138 - val_loss: 2.7274 - val_root_mean_squared_error: 1.6515\n",
      "Epoch 33/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.5838 - root_mean_squared_error: 0.7641 - val_loss: 2.5665 - val_root_mean_squared_error: 1.6020\n",
      "Epoch 34/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.5259 - root_mean_squared_error: 0.7252 - val_loss: 2.4256 - val_root_mean_squared_error: 1.5574\n",
      "Epoch 35/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.4795 - root_mean_squared_error: 0.6925 - val_loss: 2.3086 - val_root_mean_squared_error: 1.5194\n",
      "Epoch 36/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.4492 - root_mean_squared_error: 0.6702 - val_loss: 2.2003 - val_root_mean_squared_error: 1.4834\n",
      "Epoch 37/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.4230 - root_mean_squared_error: 0.6504 - val_loss: 2.1037 - val_root_mean_squared_error: 1.4504\n",
      "Epoch 38/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.4044 - root_mean_squared_error: 0.6359 - val_loss: 2.0234 - val_root_mean_squared_error: 1.4225\n",
      "Epoch 39/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.3904 - root_mean_squared_error: 0.6248 - val_loss: 1.9578 - val_root_mean_squared_error: 1.3992\n",
      "Epoch 40/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.3806 - root_mean_squared_error: 0.6169 - val_loss: 1.9007 - val_root_mean_squared_error: 1.3787\n",
      "Epoch 41/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.3731 - root_mean_squared_error: 0.6108 - val_loss: 1.8502 - val_root_mean_squared_error: 1.3602\n",
      "Epoch 42/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.3660 - root_mean_squared_error: 0.6050 - val_loss: 1.7977 - val_root_mean_squared_error: 1.3408\n",
      "Epoch 43/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.3597 - root_mean_squared_error: 0.5997 - val_loss: 1.7392 - val_root_mean_squared_error: 1.3188\n",
      "Epoch 44/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.3528 - root_mean_squared_error: 0.5940 - val_loss: 1.6875 - val_root_mean_squared_error: 1.2990\n",
      "Epoch 45/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.3482 - root_mean_squared_error: 0.5901 - val_loss: 1.6422 - val_root_mean_squared_error: 1.2815\n",
      "Epoch 46/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.3436 - root_mean_squared_error: 0.5862 - val_loss: 1.6101 - val_root_mean_squared_error: 1.2689\n",
      "Epoch 47/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.3398 - root_mean_squared_error: 0.5829 - val_loss: 1.5699 - val_root_mean_squared_error: 1.2529\n",
      "Epoch 48/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.3358 - root_mean_squared_error: 0.5795 - val_loss: 1.5332 - val_root_mean_squared_error: 1.2382\n",
      "Epoch 49/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 0s 2ms/step - loss: 0.3325 - root_mean_squared_error: 0.5767 - val_loss: 1.5057 - val_root_mean_squared_error: 1.2271\n",
      "Epoch 50/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.3291 - root_mean_squared_error: 0.5736 - val_loss: 1.4773 - val_root_mean_squared_error: 1.2155\n",
      "Epoch 51/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.3261 - root_mean_squared_error: 0.5710 - val_loss: 1.4447 - val_root_mean_squared_error: 1.2019\n",
      "Epoch 52/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.3227 - root_mean_squared_error: 0.5681 - val_loss: 1.4165 - val_root_mean_squared_error: 1.1902\n",
      "Epoch 53/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.3198 - root_mean_squared_error: 0.5655 - val_loss: 1.3875 - val_root_mean_squared_error: 1.1779\n",
      "Epoch 54/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.3168 - root_mean_squared_error: 0.5628 - val_loss: 1.3675 - val_root_mean_squared_error: 1.1694\n",
      "Epoch 55/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.3140 - root_mean_squared_error: 0.5603 - val_loss: 1.3462 - val_root_mean_squared_error: 1.1603\n",
      "Epoch 56/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.3111 - root_mean_squared_error: 0.5577 - val_loss: 1.3257 - val_root_mean_squared_error: 1.1514\n",
      "Epoch 57/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.3084 - root_mean_squared_error: 0.5554 - val_loss: 1.2975 - val_root_mean_squared_error: 1.1391\n",
      "Epoch 58/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.3055 - root_mean_squared_error: 0.5527 - val_loss: 1.2760 - val_root_mean_squared_error: 1.1296\n",
      "Epoch 59/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.3024 - root_mean_squared_error: 0.5499 - val_loss: 1.2579 - val_root_mean_squared_error: 1.1216\n",
      "Epoch 60/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2996 - root_mean_squared_error: 0.5474 - val_loss: 1.2394 - val_root_mean_squared_error: 1.1133\n",
      "Epoch 61/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.2972 - root_mean_squared_error: 0.5452 - val_loss: 1.2188 - val_root_mean_squared_error: 1.1040\n",
      "Epoch 62/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.2948 - root_mean_squared_error: 0.5429 - val_loss: 1.2034 - val_root_mean_squared_error: 1.0970\n",
      "Epoch 63/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.2926 - root_mean_squared_error: 0.5409 - val_loss: 1.1795 - val_root_mean_squared_error: 1.0860\n",
      "Epoch 64/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.2899 - root_mean_squared_error: 0.5384 - val_loss: 1.1579 - val_root_mean_squared_error: 1.0761\n",
      "Epoch 65/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.2875 - root_mean_squared_error: 0.5362 - val_loss: 1.1335 - val_root_mean_squared_error: 1.0646\n",
      "Epoch 66/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.2853 - root_mean_squared_error: 0.5341 - val_loss: 1.1142 - val_root_mean_squared_error: 1.0555\n",
      "Epoch 67/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2831 - root_mean_squared_error: 0.5320 - val_loss: 1.0914 - val_root_mean_squared_error: 1.0447\n",
      "Epoch 68/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2808 - root_mean_squared_error: 0.5299 - val_loss: 1.0704 - val_root_mean_squared_error: 1.0346\n",
      "Epoch 69/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2786 - root_mean_squared_error: 0.5279 - val_loss: 1.0512 - val_root_mean_squared_error: 1.0253\n",
      "Epoch 70/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.2766 - root_mean_squared_error: 0.5259 - val_loss: 1.0310 - val_root_mean_squared_error: 1.0154\n",
      "Epoch 71/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2745 - root_mean_squared_error: 0.5239 - val_loss: 1.0184 - val_root_mean_squared_error: 1.0092\n",
      "Epoch 72/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2723 - root_mean_squared_error: 0.5219 - val_loss: 0.9962 - val_root_mean_squared_error: 0.9981\n",
      "Epoch 73/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2700 - root_mean_squared_error: 0.5196 - val_loss: 0.9845 - val_root_mean_squared_error: 0.9922\n",
      "Epoch 74/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2678 - root_mean_squared_error: 0.5175 - val_loss: 0.9661 - val_root_mean_squared_error: 0.9829\n",
      "Epoch 75/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.2657 - root_mean_squared_error: 0.5155 - val_loss: 0.9461 - val_root_mean_squared_error: 0.9727\n",
      "Epoch 76/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2637 - root_mean_squared_error: 0.5135 - val_loss: 0.9273 - val_root_mean_squared_error: 0.9630\n",
      "Epoch 77/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2619 - root_mean_squared_error: 0.5117 - val_loss: 0.9157 - val_root_mean_squared_error: 0.9569\n",
      "Epoch 78/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.2605 - root_mean_squared_error: 0.5104 - val_loss: 0.8976 - val_root_mean_squared_error: 0.9474\n",
      "Epoch 79/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.2584 - root_mean_squared_error: 0.5083 - val_loss: 0.8868 - val_root_mean_squared_error: 0.9417\n",
      "Epoch 80/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2564 - root_mean_squared_error: 0.5063 - val_loss: 0.8726 - val_root_mean_squared_error: 0.9341\n",
      "Epoch 81/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.2546 - root_mean_squared_error: 0.5045 - val_loss: 0.8603 - val_root_mean_squared_error: 0.9275\n",
      "Epoch 82/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.2527 - root_mean_squared_error: 0.5027 - val_loss: 0.8511 - val_root_mean_squared_error: 0.9226\n",
      "Epoch 83/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2510 - root_mean_squared_error: 0.5010 - val_loss: 0.8444 - val_root_mean_squared_error: 0.9189\n",
      "Epoch 84/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2493 - root_mean_squared_error: 0.4993 - val_loss: 0.8349 - val_root_mean_squared_error: 0.9137\n",
      "Epoch 85/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2478 - root_mean_squared_error: 0.4978 - val_loss: 0.8230 - val_root_mean_squared_error: 0.9072\n",
      "Epoch 86/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.2461 - root_mean_squared_error: 0.4961 - val_loss: 0.8093 - val_root_mean_squared_error: 0.8996\n",
      "Epoch 87/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2443 - root_mean_squared_error: 0.4942 - val_loss: 0.7912 - val_root_mean_squared_error: 0.8895\n",
      "Epoch 88/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2424 - root_mean_squared_error: 0.4923 - val_loss: 0.7694 - val_root_mean_squared_error: 0.8771\n",
      "Epoch 89/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2405 - root_mean_squared_error: 0.4904 - val_loss: 0.7485 - val_root_mean_squared_error: 0.8651\n",
      "Epoch 90/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2387 - root_mean_squared_error: 0.4886 - val_loss: 0.7280 - val_root_mean_squared_error: 0.8532\n",
      "Epoch 91/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.2369 - root_mean_squared_error: 0.4868 - val_loss: 0.7169 - val_root_mean_squared_error: 0.8467\n",
      "Epoch 92/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.2353 - root_mean_squared_error: 0.4850 - val_loss: 0.7045 - val_root_mean_squared_error: 0.8393\n",
      "Epoch 93/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.2335 - root_mean_squared_error: 0.4832 - val_loss: 0.6947 - val_root_mean_squared_error: 0.8335\n",
      "Epoch 94/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.2319 - root_mean_squared_error: 0.4816 - val_loss: 0.6852 - val_root_mean_squared_error: 0.8278\n",
      "Epoch 95/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2302 - root_mean_squared_error: 0.4798 - val_loss: 0.6710 - val_root_mean_squared_error: 0.8192\n",
      "Epoch 96/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.2287 - root_mean_squared_error: 0.4783 - val_loss: 0.6578 - val_root_mean_squared_error: 0.8110\n",
      "Epoch 97/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2274 - root_mean_squared_error: 0.4769 - val_loss: 0.6472 - val_root_mean_squared_error: 0.8045\n",
      "Epoch 98/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.2258 - root_mean_squared_error: 0.4752 - val_loss: 0.6474 - val_root_mean_squared_error: 0.8046\n",
      "Epoch 99/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2246 - root_mean_squared_error: 0.4739 - val_loss: 0.6355 - val_root_mean_squared_error: 0.7972\n",
      "Epoch 100/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2232 - root_mean_squared_error: 0.4724 - val_loss: 0.6239 - val_root_mean_squared_error: 0.7899\n",
      "Epoch 101/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2216 - root_mean_squared_error: 0.4708 - val_loss: 0.6153 - val_root_mean_squared_error: 0.7844\n",
      "Epoch 102/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2204 - root_mean_squared_error: 0.4694 - val_loss: 0.6072 - val_root_mean_squared_error: 0.7792\n",
      "Epoch 103/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.2190 - root_mean_squared_error: 0.4680 - val_loss: 0.5957 - val_root_mean_squared_error: 0.7718\n",
      "Epoch 104/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2177 - root_mean_squared_error: 0.4666 - val_loss: 0.5816 - val_root_mean_squared_error: 0.7627\n",
      "Epoch 105/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.2162 - root_mean_squared_error: 0.4649 - val_loss: 0.5664 - val_root_mean_squared_error: 0.7526\n",
      "Epoch 106/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.2148 - root_mean_squared_error: 0.4635 - val_loss: 0.5550 - val_root_mean_squared_error: 0.7450\n",
      "Epoch 107/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.2137 - root_mean_squared_error: 0.4623 - val_loss: 0.5467 - val_root_mean_squared_error: 0.7394\n",
      "Epoch 108/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.2125 - root_mean_squared_error: 0.4610 - val_loss: 0.5377 - val_root_mean_squared_error: 0.7333\n",
      "Epoch 109/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.2112 - root_mean_squared_error: 0.4596 - val_loss: 0.5284 - val_root_mean_squared_error: 0.7269\n",
      "Epoch 110/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.2100 - root_mean_squared_error: 0.4582 - val_loss: 0.5231 - val_root_mean_squared_error: 0.7232\n",
      "Epoch 111/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2086 - root_mean_squared_error: 0.4568 - val_loss: 0.5145 - val_root_mean_squared_error: 0.7173\n",
      "Epoch 112/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.2075 - root_mean_squared_error: 0.4555 - val_loss: 0.5074 - val_root_mean_squared_error: 0.7123\n",
      "Epoch 113/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2063 - root_mean_squared_error: 0.4543 - val_loss: 0.5011 - val_root_mean_squared_error: 0.7079\n",
      "Epoch 114/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2053 - root_mean_squared_error: 0.4531 - val_loss: 0.4955 - val_root_mean_squared_error: 0.7039\n",
      "Epoch 115/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2039 - root_mean_squared_error: 0.4516 - val_loss: 0.4862 - val_root_mean_squared_error: 0.6973\n",
      "Epoch 116/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.2027 - root_mean_squared_error: 0.4502 - val_loss: 0.4786 - val_root_mean_squared_error: 0.6918\n",
      "Epoch 117/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.2014 - root_mean_squared_error: 0.4488 - val_loss: 0.4758 - val_root_mean_squared_error: 0.6897\n",
      "Epoch 118/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.2004 - root_mean_squared_error: 0.4476 - val_loss: 0.4695 - val_root_mean_squared_error: 0.6852\n",
      "Epoch 119/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1992 - root_mean_squared_error: 0.4463 - val_loss: 0.4594 - val_root_mean_squared_error: 0.6778\n",
      "Epoch 120/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1981 - root_mean_squared_error: 0.4451 - val_loss: 0.4493 - val_root_mean_squared_error: 0.6703\n",
      "Epoch 121/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1971 - root_mean_squared_error: 0.4440 - val_loss: 0.4404 - val_root_mean_squared_error: 0.6636\n",
      "Epoch 122/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1962 - root_mean_squared_error: 0.4430 - val_loss: 0.4341 - val_root_mean_squared_error: 0.6589\n",
      "Epoch 123/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.1951 - root_mean_squared_error: 0.4417 - val_loss: 0.4284 - val_root_mean_squared_error: 0.6546\n",
      "Epoch 124/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1938 - root_mean_squared_error: 0.4402 - val_loss: 0.4240 - val_root_mean_squared_error: 0.6511\n",
      "Epoch 125/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.1928 - root_mean_squared_error: 0.4391 - val_loss: 0.4203 - val_root_mean_squared_error: 0.6483\n",
      "Epoch 126/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1916 - root_mean_squared_error: 0.4377 - val_loss: 0.4152 - val_root_mean_squared_error: 0.6444\n",
      "Epoch 127/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1906 - root_mean_squared_error: 0.4366 - val_loss: 0.4110 - val_root_mean_squared_error: 0.6411\n",
      "Epoch 128/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1895 - root_mean_squared_error: 0.4353 - val_loss: 0.4036 - val_root_mean_squared_error: 0.6353\n",
      "Epoch 129/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1884 - root_mean_squared_error: 0.4341 - val_loss: 0.4022 - val_root_mean_squared_error: 0.6342\n",
      "Epoch 130/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1873 - root_mean_squared_error: 0.4328 - val_loss: 0.3975 - val_root_mean_squared_error: 0.6305\n",
      "Epoch 131/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.1863 - root_mean_squared_error: 0.4316 - val_loss: 0.3908 - val_root_mean_squared_error: 0.6251\n",
      "Epoch 132/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1851 - root_mean_squared_error: 0.4303 - val_loss: 0.3820 - val_root_mean_squared_error: 0.6181\n",
      "Epoch 133/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.1842 - root_mean_squared_error: 0.4292 - val_loss: 0.3757 - val_root_mean_squared_error: 0.6130\n",
      "Epoch 134/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.1832 - root_mean_squared_error: 0.4280 - val_loss: 0.3708 - val_root_mean_squared_error: 0.6089\n",
      "Epoch 135/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.1822 - root_mean_squared_error: 0.4268 - val_loss: 0.3696 - val_root_mean_squared_error: 0.6079\n",
      "Epoch 136/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.1813 - root_mean_squared_error: 0.4258 - val_loss: 0.3678 - val_root_mean_squared_error: 0.6065\n",
      "Epoch 137/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.1801 - root_mean_squared_error: 0.4244 - val_loss: 0.3603 - val_root_mean_squared_error: 0.6002\n",
      "Epoch 138/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.1790 - root_mean_squared_error: 0.4231 - val_loss: 0.3518 - val_root_mean_squared_error: 0.5932\n",
      "Epoch 139/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1781 - root_mean_squared_error: 0.4221 - val_loss: 0.3452 - val_root_mean_squared_error: 0.5876\n",
      "Epoch 140/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.1772 - root_mean_squared_error: 0.4209 - val_loss: 0.3399 - val_root_mean_squared_error: 0.5830\n",
      "Epoch 141/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.1762 - root_mean_squared_error: 0.4197 - val_loss: 0.3363 - val_root_mean_squared_error: 0.5799\n",
      "Epoch 142/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1750 - root_mean_squared_error: 0.4183 - val_loss: 0.3350 - val_root_mean_squared_error: 0.5788\n",
      "Epoch 143/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.1745 - root_mean_squared_error: 0.4177 - val_loss: 0.3332 - val_root_mean_squared_error: 0.5773\n",
      "Epoch 144/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1735 - root_mean_squared_error: 0.4166 - val_loss: 0.3290 - val_root_mean_squared_error: 0.5735\n",
      "Epoch 145/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1722 - root_mean_squared_error: 0.4150 - val_loss: 0.3178 - val_root_mean_squared_error: 0.5638\n",
      "Epoch 146/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1712 - root_mean_squared_error: 0.4137 - val_loss: 0.3071 - val_root_mean_squared_error: 0.5541\n",
      "Epoch 147/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1701 - root_mean_squared_error: 0.4124 - val_loss: 0.3021 - val_root_mean_squared_error: 0.5496\n",
      "Epoch 148/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1691 - root_mean_squared_error: 0.4113 - val_loss: 0.2941 - val_root_mean_squared_error: 0.5423\n",
      "Epoch 149/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.1682 - root_mean_squared_error: 0.4101 - val_loss: 0.2872 - val_root_mean_squared_error: 0.5359\n",
      "Epoch 150/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1674 - root_mean_squared_error: 0.4092 - val_loss: 0.2814 - val_root_mean_squared_error: 0.5305\n",
      "Epoch 151/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1665 - root_mean_squared_error: 0.4080 - val_loss: 0.2804 - val_root_mean_squared_error: 0.5295\n",
      "Epoch 152/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1655 - root_mean_squared_error: 0.4068 - val_loss: 0.2775 - val_root_mean_squared_error: 0.5268\n",
      "Epoch 153/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1645 - root_mean_squared_error: 0.4056 - val_loss: 0.2741 - val_root_mean_squared_error: 0.5236\n",
      "Epoch 154/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.1640 - root_mean_squared_error: 0.4050 - val_loss: 0.2738 - val_root_mean_squared_error: 0.5232\n",
      "Epoch 155/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1632 - root_mean_squared_error: 0.4039 - val_loss: 0.2729 - val_root_mean_squared_error: 0.5224\n",
      "Epoch 156/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1626 - root_mean_squared_error: 0.4032 - val_loss: 0.2681 - val_root_mean_squared_error: 0.5178\n",
      "Epoch 157/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1616 - root_mean_squared_error: 0.4019 - val_loss: 0.2603 - val_root_mean_squared_error: 0.5101\n",
      "Epoch 158/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1605 - root_mean_squared_error: 0.4006 - val_loss: 0.2521 - val_root_mean_squared_error: 0.5021\n",
      "Epoch 159/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.1595 - root_mean_squared_error: 0.3994 - val_loss: 0.2468 - val_root_mean_squared_error: 0.4968\n",
      "Epoch 160/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.1588 - root_mean_squared_error: 0.3985 - val_loss: 0.2411 - val_root_mean_squared_error: 0.4910\n",
      "Epoch 161/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1578 - root_mean_squared_error: 0.3972 - val_loss: 0.2371 - val_root_mean_squared_error: 0.4870\n",
      "Epoch 162/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1569 - root_mean_squared_error: 0.3961 - val_loss: 0.2348 - val_root_mean_squared_error: 0.4845\n",
      "Epoch 163/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.1560 - root_mean_squared_error: 0.3949 - val_loss: 0.2346 - val_root_mean_squared_error: 0.4844\n",
      "Epoch 164/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.1550 - root_mean_squared_error: 0.3937 - val_loss: 0.2326 - val_root_mean_squared_error: 0.4823\n",
      "Epoch 165/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.1540 - root_mean_squared_error: 0.3925 - val_loss: 0.2311 - val_root_mean_squared_error: 0.4807\n",
      "Epoch 166/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.1535 - root_mean_squared_error: 0.3918 - val_loss: 0.2323 - val_root_mean_squared_error: 0.4820\n",
      "Epoch 167/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.1525 - root_mean_squared_error: 0.3906 - val_loss: 0.2269 - val_root_mean_squared_error: 0.4763\n",
      "Epoch 168/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.1515 - root_mean_squared_error: 0.3893 - val_loss: 0.2227 - val_root_mean_squared_error: 0.4719\n",
      "Epoch 169/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1508 - root_mean_squared_error: 0.3883 - val_loss: 0.2186 - val_root_mean_squared_error: 0.4675\n",
      "Epoch 170/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.1499 - root_mean_squared_error: 0.3872 - val_loss: 0.2140 - val_root_mean_squared_error: 0.4626\n",
      "Epoch 171/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1492 - root_mean_squared_error: 0.3863 - val_loss: 0.2111 - val_root_mean_squared_error: 0.4594\n",
      "Epoch 172/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1483 - root_mean_squared_error: 0.3851 - val_loss: 0.2080 - val_root_mean_squared_error: 0.4560\n",
      "Epoch 173/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1475 - root_mean_squared_error: 0.3841 - val_loss: 0.2071 - val_root_mean_squared_error: 0.4551\n",
      "Epoch 174/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.1468 - root_mean_squared_error: 0.3831 - val_loss: 0.2034 - val_root_mean_squared_error: 0.4510\n",
      "Epoch 175/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1459 - root_mean_squared_error: 0.3820 - val_loss: 0.1992 - val_root_mean_squared_error: 0.4463\n",
      "Epoch 176/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.1451 - root_mean_squared_error: 0.3810 - val_loss: 0.1956 - val_root_mean_squared_error: 0.4422\n",
      "Epoch 177/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.1443 - root_mean_squared_error: 0.3799 - val_loss: 0.1941 - val_root_mean_squared_error: 0.4406\n",
      "Epoch 178/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1437 - root_mean_squared_error: 0.3791 - val_loss: 0.1910 - val_root_mean_squared_error: 0.4370\n",
      "Epoch 179/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.1428 - root_mean_squared_error: 0.3779 - val_loss: 0.1860 - val_root_mean_squared_error: 0.4312\n",
      "Epoch 180/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.1421 - root_mean_squared_error: 0.3769 - val_loss: 0.1826 - val_root_mean_squared_error: 0.4274\n",
      "Epoch 181/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1413 - root_mean_squared_error: 0.3759 - val_loss: 0.1787 - val_root_mean_squared_error: 0.4228\n",
      "Epoch 182/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.1405 - root_mean_squared_error: 0.3749 - val_loss: 0.1755 - val_root_mean_squared_error: 0.4190\n",
      "Epoch 183/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1398 - root_mean_squared_error: 0.3740 - val_loss: 0.1740 - val_root_mean_squared_error: 0.4172\n",
      "Epoch 184/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1390 - root_mean_squared_error: 0.3728 - val_loss: 0.1715 - val_root_mean_squared_error: 0.4141\n",
      "Epoch 185/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1385 - root_mean_squared_error: 0.3721 - val_loss: 0.1702 - val_root_mean_squared_error: 0.4125\n",
      "Epoch 186/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.1377 - root_mean_squared_error: 0.3711 - val_loss: 0.1666 - val_root_mean_squared_error: 0.4081\n",
      "Epoch 187/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.1369 - root_mean_squared_error: 0.3700 - val_loss: 0.1658 - val_root_mean_squared_error: 0.4072\n",
      "Epoch 188/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.1364 - root_mean_squared_error: 0.3693 - val_loss: 0.1629 - val_root_mean_squared_error: 0.4036\n",
      "Epoch 189/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.1356 - root_mean_squared_error: 0.3682 - val_loss: 0.1623 - val_root_mean_squared_error: 0.4029\n",
      "Epoch 190/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.1349 - root_mean_squared_error: 0.3672 - val_loss: 0.1603 - val_root_mean_squared_error: 0.4004\n",
      "Epoch 191/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.1342 - root_mean_squared_error: 0.3663 - val_loss: 0.1598 - val_root_mean_squared_error: 0.3998\n",
      "Epoch 192/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.1336 - root_mean_squared_error: 0.3654 - val_loss: 0.1583 - val_root_mean_squared_error: 0.3979\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 193/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1328 - root_mean_squared_error: 0.3644 - val_loss: 0.1565 - val_root_mean_squared_error: 0.3956\n",
      "Epoch 194/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.1321 - root_mean_squared_error: 0.3635 - val_loss: 0.1567 - val_root_mean_squared_error: 0.3958\n",
      "Epoch 195/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.1319 - root_mean_squared_error: 0.3632 - val_loss: 0.1574 - val_root_mean_squared_error: 0.3967\n",
      "Epoch 196/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.1312 - root_mean_squared_error: 0.3623 - val_loss: 0.1533 - val_root_mean_squared_error: 0.3915\n",
      "Epoch 197/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.1302 - root_mean_squared_error: 0.3609 - val_loss: 0.1479 - val_root_mean_squared_error: 0.3846\n",
      "Epoch 198/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.1293 - root_mean_squared_error: 0.3595 - val_loss: 0.1474 - val_root_mean_squared_error: 0.3839\n",
      "Epoch 199/200\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1288 - root_mean_squared_error: 0.3589 - val_loss: 0.1456 - val_root_mean_squared_error: 0.3815\n",
      "Epoch 200/200\n",
      "9/9 [==============================] - 0s 1ms/step - loss: 0.1281 - root_mean_squared_error: 0.3579 - val_loss: 0.1427 - val_root_mean_squared_error: 0.3778\n"
     ]
    }
   ],
   "source": [
    "# cnn before\n",
    "model_before_cnn = Sequential()\n",
    "model_before_cnn.add(InputLayer((7, 8)))\n",
    "model_before_cnn.add(Conv1D(32, kernel_size=2))\n",
    "model_before_cnn.add(Flatten())\n",
    "# model_cnn.add(Dropout(0.1))\n",
    "# model_cnn.add(Dense(8, 'relu'))\n",
    "\n",
    "model_before_cnn.add(Dense(1, 'linear'))\n",
    "\n",
    "# model_cnn.summary()\n",
    "model_before_cnn.compile(loss=MeanSquaredError(), optimizer=Adam(learning_rate=0.0001), metrics=[RootMeanSquaredError()])\n",
    "history = model_before_cnn.fit(X2_before_train, y2_before_train, batch_size=32, validation_data=(X2_before_val, y2_before_val), epochs=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1613,
   "id": "738d488d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/450\n",
      "9/9 [==============================] - 1s 29ms/step - loss: 222.5222 - root_mean_squared_error: 14.9172 - val_loss: 312.8474 - val_root_mean_squared_error: 17.6875\n",
      "Epoch 2/450\n",
      "9/9 [==============================] - 0s 5ms/step - loss: 215.8785 - root_mean_squared_error: 14.6928 - val_loss: 304.8492 - val_root_mean_squared_error: 17.4599\n",
      "Epoch 3/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 210.6349 - root_mean_squared_error: 14.5133 - val_loss: 300.6952 - val_root_mean_squared_error: 17.3406\n",
      "Epoch 4/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 208.2574 - root_mean_squared_error: 14.4311 - val_loss: 299.2618 - val_root_mean_squared_error: 17.2992\n",
      "Epoch 5/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 206.9252 - root_mean_squared_error: 14.3849 - val_loss: 298.9737 - val_root_mean_squared_error: 17.2909\n",
      "Epoch 6/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 206.0592 - root_mean_squared_error: 14.3548 - val_loss: 298.9432 - val_root_mean_squared_error: 17.2900\n",
      "Epoch 7/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 205.2589 - root_mean_squared_error: 14.3269 - val_loss: 298.9124 - val_root_mean_squared_error: 17.2891\n",
      "Epoch 8/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 203.9727 - root_mean_squared_error: 14.2819 - val_loss: 298.6986 - val_root_mean_squared_error: 17.2829\n",
      "Epoch 9/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 200.0369 - root_mean_squared_error: 14.1434 - val_loss: 289.5383 - val_root_mean_squared_error: 17.0158\n",
      "Epoch 10/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 190.7465 - root_mean_squared_error: 13.8111 - val_loss: 277.0858 - val_root_mean_squared_error: 16.6459\n",
      "Epoch 11/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 180.4395 - root_mean_squared_error: 13.4328 - val_loss: 264.3102 - val_root_mean_squared_error: 16.2576\n",
      "Epoch 12/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 170.1676 - root_mean_squared_error: 13.0448 - val_loss: 251.7983 - val_root_mean_squared_error: 15.8682\n",
      "Epoch 13/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 160.3519 - root_mean_squared_error: 12.6630 - val_loss: 239.7306 - val_root_mean_squared_error: 15.4832\n",
      "Epoch 14/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 150.9674 - root_mean_squared_error: 12.2869 - val_loss: 228.1904 - val_root_mean_squared_error: 15.1060\n",
      "Epoch 15/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 142.1477 - root_mean_squared_error: 11.9226 - val_loss: 217.4675 - val_root_mean_squared_error: 14.7468\n",
      "Epoch 16/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 133.9799 - root_mean_squared_error: 11.5750 - val_loss: 207.5896 - val_root_mean_squared_error: 14.4080\n",
      "Epoch 17/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 126.1952 - root_mean_squared_error: 11.2337 - val_loss: 198.2396 - val_root_mean_squared_error: 14.0798\n",
      "Epoch 18/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 118.8047 - root_mean_squared_error: 10.8998 - val_loss: 188.8535 - val_root_mean_squared_error: 13.7424\n",
      "Epoch 19/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 111.5403 - root_mean_squared_error: 10.5613 - val_loss: 179.2516 - val_root_mean_squared_error: 13.3885\n",
      "Epoch 20/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 104.5129 - root_mean_squared_error: 10.2232 - val_loss: 170.0914 - val_root_mean_squared_error: 13.0419\n",
      "Epoch 21/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 98.0543 - root_mean_squared_error: 9.9022 - val_loss: 161.6702 - val_root_mean_squared_error: 12.7150\n",
      "Epoch 22/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 92.2466 - root_mean_squared_error: 9.6045 - val_loss: 153.9911 - val_root_mean_squared_error: 12.4093\n",
      "Epoch 23/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 87.4660 - root_mean_squared_error: 9.3523 - val_loss: 147.6150 - val_root_mean_squared_error: 12.1497\n",
      "Epoch 24/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 83.6192 - root_mean_squared_error: 9.1444 - val_loss: 142.8217 - val_root_mean_squared_error: 11.9508\n",
      "Epoch 25/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 80.6883 - root_mean_squared_error: 8.9827 - val_loss: 139.1808 - val_root_mean_squared_error: 11.7975\n",
      "Epoch 26/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 78.3297 - root_mean_squared_error: 8.8504 - val_loss: 136.2449 - val_root_mean_squared_error: 11.6724\n",
      "Epoch 27/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 76.2524 - root_mean_squared_error: 8.7323 - val_loss: 133.5535 - val_root_mean_squared_error: 11.5565\n",
      "Epoch 28/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 74.0739 - root_mean_squared_error: 8.6066 - val_loss: 129.5645 - val_root_mean_squared_error: 11.3826\n",
      "Epoch 29/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 70.9952 - root_mean_squared_error: 8.4259 - val_loss: 124.7723 - val_root_mean_squared_error: 11.1702\n",
      "Epoch 30/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 67.7156 - root_mean_squared_error: 8.2289 - val_loss: 120.2557 - val_root_mean_squared_error: 10.9661\n",
      "Epoch 31/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 64.7466 - root_mean_squared_error: 8.0465 - val_loss: 116.2156 - val_root_mean_squared_error: 10.7803\n",
      "Epoch 32/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 62.1807 - root_mean_squared_error: 7.8855 - val_loss: 112.6733 - val_root_mean_squared_error: 10.6148\n",
      "Epoch 33/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 60.0233 - root_mean_squared_error: 7.7475 - val_loss: 109.8113 - val_root_mean_squared_error: 10.4791\n",
      "Epoch 34/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 58.3038 - root_mean_squared_error: 7.6357 - val_loss: 107.4697 - val_root_mean_squared_error: 10.3668\n",
      "Epoch 35/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 56.8701 - root_mean_squared_error: 7.5412 - val_loss: 105.5013 - val_root_mean_squared_error: 10.2714\n",
      "Epoch 36/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 55.6045 - root_mean_squared_error: 7.4568 - val_loss: 103.7593 - val_root_mean_squared_error: 10.1862\n",
      "Epoch 37/450\n",
      "9/9 [==============================] - 0s 5ms/step - loss: 54.4545 - root_mean_squared_error: 7.3793 - val_loss: 102.1613 - val_root_mean_squared_error: 10.1075\n",
      "Epoch 38/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 53.3875 - root_mean_squared_error: 7.3067 - val_loss: 100.6306 - val_root_mean_squared_error: 10.0315\n",
      "Epoch 39/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 52.3536 - root_mean_squared_error: 7.2356 - val_loss: 99.1816 - val_root_mean_squared_error: 9.9590\n",
      "Epoch 40/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 51.3739 - root_mean_squared_error: 7.1676 - val_loss: 97.7600 - val_root_mean_squared_error: 9.8874\n",
      "Epoch 41/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 50.4185 - root_mean_squared_error: 7.1006 - val_loss: 96.3791 - val_root_mean_squared_error: 9.8173\n",
      "Epoch 42/450\n",
      "9/9 [==============================] - 0s 5ms/step - loss: 49.4800 - root_mean_squared_error: 7.0342 - val_loss: 95.0500 - val_root_mean_squared_error: 9.7494\n",
      "Epoch 43/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 48.5812 - root_mean_squared_error: 6.9700 - val_loss: 93.7349 - val_root_mean_squared_error: 9.6817\n",
      "Epoch 44/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 47.6895 - root_mean_squared_error: 6.9058 - val_loss: 92.4342 - val_root_mean_squared_error: 9.6143\n",
      "Epoch 45/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 46.8087 - root_mean_squared_error: 6.8417 - val_loss: 91.1622 - val_root_mean_squared_error: 9.5479\n",
      "Epoch 46/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 45.9627 - root_mean_squared_error: 6.7796 - val_loss: 89.8915 - val_root_mean_squared_error: 9.4811\n",
      "Epoch 47/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 45.1130 - root_mean_squared_error: 6.7166 - val_loss: 88.6527 - val_root_mean_squared_error: 9.4156\n",
      "Epoch 48/450\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 0s 4ms/step - loss: 44.2802 - root_mean_squared_error: 6.6543 - val_loss: 87.4077 - val_root_mean_squared_error: 9.3492\n",
      "Epoch 49/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 43.4496 - root_mean_squared_error: 6.5916 - val_loss: 86.1604 - val_root_mean_squared_error: 9.2823\n",
      "Epoch 50/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 42.6215 - root_mean_squared_error: 6.5285 - val_loss: 84.9075 - val_root_mean_squared_error: 9.2145\n",
      "Epoch 51/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 41.7883 - root_mean_squared_error: 6.4644 - val_loss: 83.6589 - val_root_mean_squared_error: 9.1465\n",
      "Epoch 52/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 40.9639 - root_mean_squared_error: 6.4003 - val_loss: 82.3912 - val_root_mean_squared_error: 9.0770\n",
      "Epoch 53/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 40.1227 - root_mean_squared_error: 6.3343 - val_loss: 81.1156 - val_root_mean_squared_error: 9.0064\n",
      "Epoch 54/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 39.2845 - root_mean_squared_error: 6.2677 - val_loss: 79.8823 - val_root_mean_squared_error: 8.9377\n",
      "Epoch 55/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 38.4829 - root_mean_squared_error: 6.2035 - val_loss: 78.6830 - val_root_mean_squared_error: 8.8703\n",
      "Epoch 56/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 37.7074 - root_mean_squared_error: 6.1406 - val_loss: 77.5501 - val_root_mean_squared_error: 8.8063\n",
      "Epoch 57/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 36.9723 - root_mean_squared_error: 6.0805 - val_loss: 76.4633 - val_root_mean_squared_error: 8.7443\n",
      "Epoch 58/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 36.2824 - root_mean_squared_error: 6.0235 - val_loss: 75.3899 - val_root_mean_squared_error: 8.6827\n",
      "Epoch 59/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 35.6075 - root_mean_squared_error: 5.9672 - val_loss: 74.3618 - val_root_mean_squared_error: 8.6233\n",
      "Epoch 60/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 34.9600 - root_mean_squared_error: 5.9127 - val_loss: 73.3787 - val_root_mean_squared_error: 8.5661\n",
      "Epoch 61/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 34.3456 - root_mean_squared_error: 5.8605 - val_loss: 72.4094 - val_root_mean_squared_error: 8.5094\n",
      "Epoch 62/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 33.7258 - root_mean_squared_error: 5.8074 - val_loss: 71.4570 - val_root_mean_squared_error: 8.4532\n",
      "Epoch 63/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 33.1288 - root_mean_squared_error: 5.7558 - val_loss: 70.5077 - val_root_mean_squared_error: 8.3969\n",
      "Epoch 64/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 32.5312 - root_mean_squared_error: 5.7036 - val_loss: 69.5698 - val_root_mean_squared_error: 8.3408\n",
      "Epoch 65/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 31.9485 - root_mean_squared_error: 5.6523 - val_loss: 68.6338 - val_root_mean_squared_error: 8.2846\n",
      "Epoch 66/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 31.3690 - root_mean_squared_error: 5.6008 - val_loss: 67.7172 - val_root_mean_squared_error: 8.2290\n",
      "Epoch 67/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 30.8066 - root_mean_squared_error: 5.5504 - val_loss: 66.8172 - val_root_mean_squared_error: 8.1742\n",
      "Epoch 68/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 30.2563 - root_mean_squared_error: 5.5006 - val_loss: 65.9401 - val_root_mean_squared_error: 8.1204\n",
      "Epoch 69/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 29.7157 - root_mean_squared_error: 5.4512 - val_loss: 65.0653 - val_root_mean_squared_error: 8.0663\n",
      "Epoch 70/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 29.1849 - root_mean_squared_error: 5.4023 - val_loss: 64.1917 - val_root_mean_squared_error: 8.0120\n",
      "Epoch 71/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 28.6570 - root_mean_squared_error: 5.3532 - val_loss: 63.3537 - val_root_mean_squared_error: 7.9595\n",
      "Epoch 72/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 28.1461 - root_mean_squared_error: 5.3053 - val_loss: 62.5172 - val_root_mean_squared_error: 7.9068\n",
      "Epoch 73/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 27.6424 - root_mean_squared_error: 5.2576 - val_loss: 61.6930 - val_root_mean_squared_error: 7.8545\n",
      "Epoch 74/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 27.1579 - root_mean_squared_error: 5.2113 - val_loss: 60.8749 - val_root_mean_squared_error: 7.8022\n",
      "Epoch 75/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 26.6625 - root_mean_squared_error: 5.1636 - val_loss: 60.0897 - val_root_mean_squared_error: 7.7518\n",
      "Epoch 76/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 26.2006 - root_mean_squared_error: 5.1186 - val_loss: 59.3086 - val_root_mean_squared_error: 7.7012\n",
      "Epoch 77/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 25.7344 - root_mean_squared_error: 5.0729 - val_loss: 58.5196 - val_root_mean_squared_error: 7.6498\n",
      "Epoch 78/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 25.2729 - root_mean_squared_error: 5.0272 - val_loss: 57.7420 - val_root_mean_squared_error: 7.5988\n",
      "Epoch 79/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 24.8178 - root_mean_squared_error: 4.9817 - val_loss: 56.9767 - val_root_mean_squared_error: 7.5483\n",
      "Epoch 80/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 24.3659 - root_mean_squared_error: 4.9362 - val_loss: 56.2072 - val_root_mean_squared_error: 7.4971\n",
      "Epoch 81/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 23.9181 - root_mean_squared_error: 4.8906 - val_loss: 55.4502 - val_root_mean_squared_error: 7.4465\n",
      "Epoch 82/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 23.4840 - root_mean_squared_error: 4.8460 - val_loss: 54.6902 - val_root_mean_squared_error: 7.3953\n",
      "Epoch 83/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 23.0512 - root_mean_squared_error: 4.8012 - val_loss: 53.9615 - val_root_mean_squared_error: 7.3459\n",
      "Epoch 84/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 22.6361 - root_mean_squared_error: 4.7577 - val_loss: 53.2426 - val_root_mean_squared_error: 7.2967\n",
      "Epoch 85/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 22.2232 - root_mean_squared_error: 4.7141 - val_loss: 52.5291 - val_root_mean_squared_error: 7.2477\n",
      "Epoch 86/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 21.8243 - root_mean_squared_error: 4.6716 - val_loss: 51.8271 - val_root_mean_squared_error: 7.1991\n",
      "Epoch 87/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 21.4357 - root_mean_squared_error: 4.6299 - val_loss: 51.1645 - val_root_mean_squared_error: 7.1529\n",
      "Epoch 88/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 21.0581 - root_mean_squared_error: 4.5889 - val_loss: 50.4911 - val_root_mean_squared_error: 7.1057\n",
      "Epoch 89/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 20.6748 - root_mean_squared_error: 4.5470 - val_loss: 49.8070 - val_root_mean_squared_error: 7.0574\n",
      "Epoch 90/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 20.2975 - root_mean_squared_error: 4.5053 - val_loss: 49.1239 - val_root_mean_squared_error: 7.0088\n",
      "Epoch 91/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 19.9229 - root_mean_squared_error: 4.4635 - val_loss: 48.4625 - val_root_mean_squared_error: 6.9615\n",
      "Epoch 92/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 19.5637 - root_mean_squared_error: 4.4231 - val_loss: 47.8178 - val_root_mean_squared_error: 6.9150\n",
      "Epoch 93/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 19.2097 - root_mean_squared_error: 4.3829 - val_loss: 47.1686 - val_root_mean_squared_error: 6.8679\n",
      "Epoch 94/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 18.8642 - root_mean_squared_error: 4.3433 - val_loss: 46.5341 - val_root_mean_squared_error: 6.8216\n",
      "Epoch 95/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 18.5203 - root_mean_squared_error: 4.3035 - val_loss: 45.9170 - val_root_mean_squared_error: 6.7762\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 96/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 18.1796 - root_mean_squared_error: 4.2638 - val_loss: 45.2916 - val_root_mean_squared_error: 6.7299\n",
      "Epoch 97/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 17.8488 - root_mean_squared_error: 4.2248 - val_loss: 44.6667 - val_root_mean_squared_error: 6.6833\n",
      "Epoch 98/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 17.5241 - root_mean_squared_error: 4.1862 - val_loss: 44.0616 - val_root_mean_squared_error: 6.6379\n",
      "Epoch 99/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 17.2027 - root_mean_squared_error: 4.1476 - val_loss: 43.4590 - val_root_mean_squared_error: 6.5923\n",
      "Epoch 100/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 16.8856 - root_mean_squared_error: 4.1092 - val_loss: 42.8708 - val_root_mean_squared_error: 6.5476\n",
      "Epoch 101/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 16.5835 - root_mean_squared_error: 4.0723 - val_loss: 42.2936 - val_root_mean_squared_error: 6.5034\n",
      "Epoch 102/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 16.2944 - root_mean_squared_error: 4.0366 - val_loss: 41.7293 - val_root_mean_squared_error: 6.4598\n",
      "Epoch 103/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 16.0023 - root_mean_squared_error: 4.0003 - val_loss: 41.1838 - val_root_mean_squared_error: 6.4175\n",
      "Epoch 104/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 15.7161 - root_mean_squared_error: 3.9643 - val_loss: 40.6308 - val_root_mean_squared_error: 6.3742\n",
      "Epoch 105/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 15.4304 - root_mean_squared_error: 3.9281 - val_loss: 40.0737 - val_root_mean_squared_error: 6.3304\n",
      "Epoch 106/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 15.1513 - root_mean_squared_error: 3.8925 - val_loss: 39.5225 - val_root_mean_squared_error: 6.2867\n",
      "Epoch 107/450\n",
      "9/9 [==============================] - 0s 5ms/step - loss: 14.8757 - root_mean_squared_error: 3.8569 - val_loss: 38.9799 - val_root_mean_squared_error: 6.2434\n",
      "Epoch 108/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 14.6088 - root_mean_squared_error: 3.8222 - val_loss: 38.4476 - val_root_mean_squared_error: 6.2006\n",
      "Epoch 109/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 14.3458 - root_mean_squared_error: 3.7876 - val_loss: 37.9170 - val_root_mean_squared_error: 6.1577\n",
      "Epoch 110/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 14.0919 - root_mean_squared_error: 3.7539 - val_loss: 37.3954 - val_root_mean_squared_error: 6.1152\n",
      "Epoch 111/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 13.8353 - root_mean_squared_error: 3.7196 - val_loss: 36.8863 - val_root_mean_squared_error: 6.0734\n",
      "Epoch 112/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 13.5875 - root_mean_squared_error: 3.6861 - val_loss: 36.3772 - val_root_mean_squared_error: 6.0313\n",
      "Epoch 113/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 13.3468 - root_mean_squared_error: 3.6533 - val_loss: 35.8854 - val_root_mean_squared_error: 5.9904\n",
      "Epoch 114/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 13.1109 - root_mean_squared_error: 3.6209 - val_loss: 35.4035 - val_root_mean_squared_error: 5.9501\n",
      "Epoch 115/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 12.8925 - root_mean_squared_error: 3.5906 - val_loss: 34.9398 - val_root_mean_squared_error: 5.9110\n",
      "Epoch 116/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 12.6764 - root_mean_squared_error: 3.5604 - val_loss: 34.4926 - val_root_mean_squared_error: 5.8730\n",
      "Epoch 117/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 12.4623 - root_mean_squared_error: 3.5302 - val_loss: 34.0337 - val_root_mean_squared_error: 5.8338\n",
      "Epoch 118/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 12.2499 - root_mean_squared_error: 3.5000 - val_loss: 33.5700 - val_root_mean_squared_error: 5.7940\n",
      "Epoch 119/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 12.0366 - root_mean_squared_error: 3.4694 - val_loss: 33.1150 - val_root_mean_squared_error: 5.7546\n",
      "Epoch 120/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 11.8278 - root_mean_squared_error: 3.4392 - val_loss: 32.6567 - val_root_mean_squared_error: 5.7146\n",
      "Epoch 121/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 11.6256 - root_mean_squared_error: 3.4096 - val_loss: 32.2187 - val_root_mean_squared_error: 5.6762\n",
      "Epoch 122/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 11.4318 - root_mean_squared_error: 3.3811 - val_loss: 31.8071 - val_root_mean_squared_error: 5.6398\n",
      "Epoch 123/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 11.2494 - root_mean_squared_error: 3.3540 - val_loss: 31.3941 - val_root_mean_squared_error: 5.6030\n",
      "Epoch 124/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 11.0686 - root_mean_squared_error: 3.3270 - val_loss: 30.9691 - val_root_mean_squared_error: 5.5650\n",
      "Epoch 125/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 10.8803 - root_mean_squared_error: 3.2985 - val_loss: 30.5596 - val_root_mean_squared_error: 5.5281\n",
      "Epoch 126/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 10.7065 - root_mean_squared_error: 3.2721 - val_loss: 30.1684 - val_root_mean_squared_error: 5.4926\n",
      "Epoch 127/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 10.5361 - root_mean_squared_error: 3.2459 - val_loss: 29.7682 - val_root_mean_squared_error: 5.4560\n",
      "Epoch 128/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 10.3627 - root_mean_squared_error: 3.2191 - val_loss: 29.3721 - val_root_mean_squared_error: 5.4196\n",
      "Epoch 129/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 10.1965 - root_mean_squared_error: 3.1932 - val_loss: 28.9719 - val_root_mean_squared_error: 5.3826\n",
      "Epoch 130/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 10.0303 - root_mean_squared_error: 3.1671 - val_loss: 28.5825 - val_root_mean_squared_error: 5.3463\n",
      "Epoch 131/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 9.8685 - root_mean_squared_error: 3.1414 - val_loss: 28.1941 - val_root_mean_squared_error: 5.3098\n",
      "Epoch 132/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 9.7123 - root_mean_squared_error: 3.1165 - val_loss: 27.8265 - val_root_mean_squared_error: 5.2751\n",
      "Epoch 133/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 9.5653 - root_mean_squared_error: 3.0928 - val_loss: 27.4643 - val_root_mean_squared_error: 5.2406\n",
      "Epoch 134/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 9.4139 - root_mean_squared_error: 3.0682 - val_loss: 27.1029 - val_root_mean_squared_error: 5.2060\n",
      "Epoch 135/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 9.2732 - root_mean_squared_error: 3.0452 - val_loss: 26.7486 - val_root_mean_squared_error: 5.1719\n",
      "Epoch 136/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 9.1322 - root_mean_squared_error: 3.0220 - val_loss: 26.3964 - val_root_mean_squared_error: 5.1377\n",
      "Epoch 137/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 8.9913 - root_mean_squared_error: 2.9986 - val_loss: 26.0471 - val_root_mean_squared_error: 5.1036\n",
      "Epoch 138/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 8.8511 - root_mean_squared_error: 2.9751 - val_loss: 25.7191 - val_root_mean_squared_error: 5.0714\n",
      "Epoch 139/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 8.7127 - root_mean_squared_error: 2.9517 - val_loss: 25.4278 - val_root_mean_squared_error: 5.0426\n",
      "Epoch 140/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 8.5592 - root_mean_squared_error: 2.9256 - val_loss: 25.1288 - val_root_mean_squared_error: 5.0129\n",
      "Epoch 141/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 8.3885 - root_mean_squared_error: 2.8963 - val_loss: 24.8043 - val_root_mean_squared_error: 4.9804\n",
      "Epoch 142/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 8.2151 - root_mean_squared_error: 2.8662 - val_loss: 24.5118 - val_root_mean_squared_error: 4.9509\n",
      "Epoch 143/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 8.0528 - root_mean_squared_error: 2.8377 - val_loss: 24.1845 - val_root_mean_squared_error: 4.9178\n",
      "Epoch 144/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 7.8684 - root_mean_squared_error: 2.8051 - val_loss: 23.8425 - val_root_mean_squared_error: 4.8829\n",
      "Epoch 145/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 7.6799 - root_mean_squared_error: 2.7713 - val_loss: 23.4904 - val_root_mean_squared_error: 4.8467\n",
      "Epoch 146/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 7.4725 - root_mean_squared_error: 2.7336 - val_loss: 23.1794 - val_root_mean_squared_error: 4.8145\n",
      "Epoch 147/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 7.2878 - root_mean_squared_error: 2.6996 - val_loss: 22.8433 - val_root_mean_squared_error: 4.7795\n",
      "Epoch 148/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 7.1009 - root_mean_squared_error: 2.6648 - val_loss: 22.5229 - val_root_mean_squared_error: 4.7458\n",
      "Epoch 149/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 6.9158 - root_mean_squared_error: 2.6298 - val_loss: 22.1973 - val_root_mean_squared_error: 4.7114\n",
      "Epoch 150/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 6.7093 - root_mean_squared_error: 2.5902 - val_loss: 21.9089 - val_root_mean_squared_error: 4.6807\n",
      "Epoch 151/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 6.5321 - root_mean_squared_error: 2.5558 - val_loss: 21.5961 - val_root_mean_squared_error: 4.6472\n",
      "Epoch 152/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 6.3688 - root_mean_squared_error: 2.5236 - val_loss: 21.3073 - val_root_mean_squared_error: 4.6160\n",
      "Epoch 153/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 6.2183 - root_mean_squared_error: 2.4937 - val_loss: 21.0202 - val_root_mean_squared_error: 4.5848\n",
      "Epoch 154/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 6.0899 - root_mean_squared_error: 2.4678 - val_loss: 20.7476 - val_root_mean_squared_error: 4.5550\n",
      "Epoch 155/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 5.9553 - root_mean_squared_error: 2.4404 - val_loss: 20.4611 - val_root_mean_squared_error: 4.5234\n",
      "Epoch 156/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 5.8264 - root_mean_squared_error: 2.4138 - val_loss: 20.1819 - val_root_mean_squared_error: 4.4924\n",
      "Epoch 157/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 5.7119 - root_mean_squared_error: 2.3900 - val_loss: 19.9019 - val_root_mean_squared_error: 4.4612\n",
      "Epoch 158/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 5.5916 - root_mean_squared_error: 2.3647 - val_loss: 19.6396 - val_root_mean_squared_error: 4.4317\n",
      "Epoch 159/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 5.4563 - root_mean_squared_error: 2.3359 - val_loss: 19.3833 - val_root_mean_squared_error: 4.4026\n",
      "Epoch 160/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 5.2910 - root_mean_squared_error: 2.3002 - val_loss: 19.1290 - val_root_mean_squared_error: 4.3737\n",
      "Epoch 161/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 5.1536 - root_mean_squared_error: 2.2702 - val_loss: 18.8595 - val_root_mean_squared_error: 4.3428\n",
      "Epoch 162/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 5.0177 - root_mean_squared_error: 2.2400 - val_loss: 18.6052 - val_root_mean_squared_error: 4.3134\n",
      "Epoch 163/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 4.8968 - root_mean_squared_error: 2.2129 - val_loss: 18.3278 - val_root_mean_squared_error: 4.2811\n",
      "Epoch 164/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 4.7821 - root_mean_squared_error: 2.1868 - val_loss: 18.0723 - val_root_mean_squared_error: 4.2511\n",
      "Epoch 165/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 4.6780 - root_mean_squared_error: 2.1629 - val_loss: 17.8206 - val_root_mean_squared_error: 4.2214\n",
      "Epoch 166/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 4.5796 - root_mean_squared_error: 2.1400 - val_loss: 17.5884 - val_root_mean_squared_error: 4.1939\n",
      "Epoch 167/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 4.4919 - root_mean_squared_error: 2.1194 - val_loss: 17.3522 - val_root_mean_squared_error: 4.1656\n",
      "Epoch 168/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 4.4026 - root_mean_squared_error: 2.0982 - val_loss: 17.1158 - val_root_mean_squared_error: 4.1371\n",
      "Epoch 169/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 4.3195 - root_mean_squared_error: 2.0783 - val_loss: 16.8719 - val_root_mean_squared_error: 4.1075\n",
      "Epoch 170/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 4.2254 - root_mean_squared_error: 2.0556 - val_loss: 16.6347 - val_root_mean_squared_error: 4.0786\n",
      "Epoch 171/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 4.1448 - root_mean_squared_error: 2.0359 - val_loss: 16.4038 - val_root_mean_squared_error: 4.0502\n",
      "Epoch 172/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 4.0572 - root_mean_squared_error: 2.0142 - val_loss: 16.1748 - val_root_mean_squared_error: 4.0218\n",
      "Epoch 173/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 3.9787 - root_mean_squared_error: 1.9947 - val_loss: 15.9657 - val_root_mean_squared_error: 3.9957\n",
      "Epoch 174/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 3.9067 - root_mean_squared_error: 1.9765 - val_loss: 15.7574 - val_root_mean_squared_error: 3.9696\n",
      "Epoch 175/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 3.8359 - root_mean_squared_error: 1.9585 - val_loss: 15.5615 - val_root_mean_squared_error: 3.9448\n",
      "Epoch 176/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 3.7696 - root_mean_squared_error: 1.9416 - val_loss: 15.3808 - val_root_mean_squared_error: 3.9218\n",
      "Epoch 177/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 3.6790 - root_mean_squared_error: 1.9181 - val_loss: 15.1871 - val_root_mean_squared_error: 3.8971\n",
      "Epoch 178/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 3.5592 - root_mean_squared_error: 1.8866 - val_loss: 14.9931 - val_root_mean_squared_error: 3.8721\n",
      "Epoch 179/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 3.4299 - root_mean_squared_error: 1.8520 - val_loss: 14.7868 - val_root_mean_squared_error: 3.8454\n",
      "Epoch 180/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 3.3203 - root_mean_squared_error: 1.8222 - val_loss: 14.5839 - val_root_mean_squared_error: 3.8189\n",
      "Epoch 181/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 3.2256 - root_mean_squared_error: 1.7960 - val_loss: 14.3751 - val_root_mean_squared_error: 3.7914\n",
      "Epoch 182/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 3.1356 - root_mean_squared_error: 1.7708 - val_loss: 14.1793 - val_root_mean_squared_error: 3.7655\n",
      "Epoch 183/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 3.0585 - root_mean_squared_error: 1.7489 - val_loss: 13.9878 - val_root_mean_squared_error: 3.7400\n",
      "Epoch 184/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 2.9912 - root_mean_squared_error: 1.7295 - val_loss: 13.7962 - val_root_mean_squared_error: 3.7143\n",
      "Epoch 185/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 2.9258 - root_mean_squared_error: 1.7105 - val_loss: 13.6117 - val_root_mean_squared_error: 3.6894\n",
      "Epoch 186/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 2.8619 - root_mean_squared_error: 1.6917 - val_loss: 13.4362 - val_root_mean_squared_error: 3.6655\n",
      "Epoch 187/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 2.8030 - root_mean_squared_error: 1.6742 - val_loss: 13.2532 - val_root_mean_squared_error: 3.6405\n",
      "Epoch 188/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 2.7439 - root_mean_squared_error: 1.6565 - val_loss: 13.0746 - val_root_mean_squared_error: 3.6159\n",
      "Epoch 189/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 2.6877 - root_mean_squared_error: 1.6394 - val_loss: 12.8990 - val_root_mean_squared_error: 3.5915\n",
      "Epoch 190/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 2.6316 - root_mean_squared_error: 1.6222 - val_loss: 12.7228 - val_root_mean_squared_error: 3.5669\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 191/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 2.5789 - root_mean_squared_error: 1.6059 - val_loss: 12.5520 - val_root_mean_squared_error: 3.5429\n",
      "Epoch 192/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 2.5265 - root_mean_squared_error: 1.5895 - val_loss: 12.3867 - val_root_mean_squared_error: 3.5195\n",
      "Epoch 193/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 2.4759 - root_mean_squared_error: 1.5735 - val_loss: 12.2388 - val_root_mean_squared_error: 3.4984\n",
      "Epoch 194/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 2.4315 - root_mean_squared_error: 1.5593 - val_loss: 12.0922 - val_root_mean_squared_error: 3.4774\n",
      "Epoch 195/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 2.3860 - root_mean_squared_error: 1.5447 - val_loss: 11.9422 - val_root_mean_squared_error: 3.4558\n",
      "Epoch 196/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 2.3435 - root_mean_squared_error: 1.5308 - val_loss: 11.7971 - val_root_mean_squared_error: 3.4347\n",
      "Epoch 197/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 2.3023 - root_mean_squared_error: 1.5173 - val_loss: 11.6504 - val_root_mean_squared_error: 3.4133\n",
      "Epoch 198/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 2.2535 - root_mean_squared_error: 1.5012 - val_loss: 11.4900 - val_root_mean_squared_error: 3.3897\n",
      "Epoch 199/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 2.2099 - root_mean_squared_error: 1.4866 - val_loss: 11.3516 - val_root_mean_squared_error: 3.3692\n",
      "Epoch 200/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 2.1670 - root_mean_squared_error: 1.4721 - val_loss: 11.2207 - val_root_mean_squared_error: 3.3497\n",
      "Epoch 201/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 2.1139 - root_mean_squared_error: 1.4539 - val_loss: 11.1054 - val_root_mean_squared_error: 3.3325\n",
      "Epoch 202/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 2.0422 - root_mean_squared_error: 1.4291 - val_loss: 10.9695 - val_root_mean_squared_error: 3.3120\n",
      "Epoch 203/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 1.9595 - root_mean_squared_error: 1.3998 - val_loss: 10.8263 - val_root_mean_squared_error: 3.2903\n",
      "Epoch 204/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 1.9001 - root_mean_squared_error: 1.3785 - val_loss: 10.6852 - val_root_mean_squared_error: 3.2688\n",
      "Epoch 205/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 1.8517 - root_mean_squared_error: 1.3608 - val_loss: 10.5476 - val_root_mean_squared_error: 3.2477\n",
      "Epoch 206/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 1.8110 - root_mean_squared_error: 1.3457 - val_loss: 10.4130 - val_root_mean_squared_error: 3.2269\n",
      "Epoch 207/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 1.7704 - root_mean_squared_error: 1.3306 - val_loss: 10.2759 - val_root_mean_squared_error: 3.2056\n",
      "Epoch 208/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 1.7340 - root_mean_squared_error: 1.3168 - val_loss: 10.1393 - val_root_mean_squared_error: 3.1842\n",
      "Epoch 209/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 1.6953 - root_mean_squared_error: 1.3020 - val_loss: 10.0052 - val_root_mean_squared_error: 3.1631\n",
      "Epoch 210/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 1.6655 - root_mean_squared_error: 1.2905 - val_loss: 9.8874 - val_root_mean_squared_error: 3.1444\n",
      "Epoch 211/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 1.6320 - root_mean_squared_error: 1.2775 - val_loss: 9.7637 - val_root_mean_squared_error: 3.1247\n",
      "Epoch 212/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 1.5977 - root_mean_squared_error: 1.2640 - val_loss: 9.6460 - val_root_mean_squared_error: 3.1058\n",
      "Epoch 213/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 1.5656 - root_mean_squared_error: 1.2512 - val_loss: 9.5337 - val_root_mean_squared_error: 3.0877\n",
      "Epoch 214/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 1.5282 - root_mean_squared_error: 1.2362 - val_loss: 9.4357 - val_root_mean_squared_error: 3.0718\n",
      "Epoch 215/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 1.4782 - root_mean_squared_error: 1.2158 - val_loss: 9.3091 - val_root_mean_squared_error: 3.0511\n",
      "Epoch 216/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 1.4312 - root_mean_squared_error: 1.1963 - val_loss: 9.1863 - val_root_mean_squared_error: 3.0309\n",
      "Epoch 217/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 1.3949 - root_mean_squared_error: 1.1811 - val_loss: 9.0709 - val_root_mean_squared_error: 3.0118\n",
      "Epoch 218/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 1.3532 - root_mean_squared_error: 1.1633 - val_loss: 8.9596 - val_root_mean_squared_error: 2.9933\n",
      "Epoch 219/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 1.3241 - root_mean_squared_error: 1.1507 - val_loss: 8.8486 - val_root_mean_squared_error: 2.9747\n",
      "Epoch 220/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 1.2966 - root_mean_squared_error: 1.1387 - val_loss: 8.7362 - val_root_mean_squared_error: 2.9557\n",
      "Epoch 221/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 1.2684 - root_mean_squared_error: 1.1262 - val_loss: 8.6273 - val_root_mean_squared_error: 2.9372\n",
      "Epoch 222/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 1.2407 - root_mean_squared_error: 1.1139 - val_loss: 8.5195 - val_root_mean_squared_error: 2.9188\n",
      "Epoch 223/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 1.2190 - root_mean_squared_error: 1.1041 - val_loss: 8.4111 - val_root_mean_squared_error: 2.9002\n",
      "Epoch 224/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 1.1955 - root_mean_squared_error: 1.0934 - val_loss: 8.3110 - val_root_mean_squared_error: 2.8829\n",
      "Epoch 225/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 1.1715 - root_mean_squared_error: 1.0823 - val_loss: 8.2147 - val_root_mean_squared_error: 2.8661\n",
      "Epoch 226/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 1.1491 - root_mean_squared_error: 1.0720 - val_loss: 8.1217 - val_root_mean_squared_error: 2.8499\n",
      "Epoch 227/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 1.1235 - root_mean_squared_error: 1.0599 - val_loss: 8.0245 - val_root_mean_squared_error: 2.8328\n",
      "Epoch 228/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 1.1022 - root_mean_squared_error: 1.0499 - val_loss: 7.9317 - val_root_mean_squared_error: 2.8163\n",
      "Epoch 229/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 1.0835 - root_mean_squared_error: 1.0409 - val_loss: 7.8433 - val_root_mean_squared_error: 2.8006\n",
      "Epoch 230/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 1.0638 - root_mean_squared_error: 1.0314 - val_loss: 7.7573 - val_root_mean_squared_error: 2.7852\n",
      "Epoch 231/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 1.0450 - root_mean_squared_error: 1.0222 - val_loss: 7.6704 - val_root_mean_squared_error: 2.7695\n",
      "Epoch 232/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 1.0271 - root_mean_squared_error: 1.0134 - val_loss: 7.5759 - val_root_mean_squared_error: 2.7524\n",
      "Epoch 233/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 1.0071 - root_mean_squared_error: 1.0036 - val_loss: 7.4932 - val_root_mean_squared_error: 2.7374\n",
      "Epoch 234/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.9892 - root_mean_squared_error: 0.9946 - val_loss: 7.4139 - val_root_mean_squared_error: 2.7229\n",
      "Epoch 235/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.9724 - root_mean_squared_error: 0.9861 - val_loss: 7.3235 - val_root_mean_squared_error: 2.7062\n",
      "Epoch 236/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.9557 - root_mean_squared_error: 0.9776 - val_loss: 7.2400 - val_root_mean_squared_error: 2.6907\n",
      "Epoch 237/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.9384 - root_mean_squared_error: 0.9687 - val_loss: 7.1551 - val_root_mean_squared_error: 2.6749\n",
      "Epoch 238/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.9199 - root_mean_squared_error: 0.9591 - val_loss: 7.0769 - val_root_mean_squared_error: 2.6602\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 239/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.9063 - root_mean_squared_error: 0.9520 - val_loss: 6.9940 - val_root_mean_squared_error: 2.6446\n",
      "Epoch 240/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.8903 - root_mean_squared_error: 0.9435 - val_loss: 6.9233 - val_root_mean_squared_error: 2.6312\n",
      "Epoch 241/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.8729 - root_mean_squared_error: 0.9343 - val_loss: 6.8375 - val_root_mean_squared_error: 2.6149\n",
      "Epoch 242/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.8591 - root_mean_squared_error: 0.9269 - val_loss: 6.7625 - val_root_mean_squared_error: 2.6005\n",
      "Epoch 243/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.8424 - root_mean_squared_error: 0.9178 - val_loss: 6.6823 - val_root_mean_squared_error: 2.5850\n",
      "Epoch 244/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.8278 - root_mean_squared_error: 0.9098 - val_loss: 6.6135 - val_root_mean_squared_error: 2.5717\n",
      "Epoch 245/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.8142 - root_mean_squared_error: 0.9023 - val_loss: 6.5332 - val_root_mean_squared_error: 2.5560\n",
      "Epoch 246/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.8015 - root_mean_squared_error: 0.8952 - val_loss: 6.4661 - val_root_mean_squared_error: 2.5429\n",
      "Epoch 247/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.7891 - root_mean_squared_error: 0.8883 - val_loss: 6.3846 - val_root_mean_squared_error: 2.5268\n",
      "Epoch 248/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.7747 - root_mean_squared_error: 0.8802 - val_loss: 6.3234 - val_root_mean_squared_error: 2.5146\n",
      "Epoch 249/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.7634 - root_mean_squared_error: 0.8737 - val_loss: 6.2446 - val_root_mean_squared_error: 2.4989\n",
      "Epoch 250/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.7524 - root_mean_squared_error: 0.8674 - val_loss: 6.1865 - val_root_mean_squared_error: 2.4873\n",
      "Epoch 251/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.7394 - root_mean_squared_error: 0.8599 - val_loss: 6.1257 - val_root_mean_squared_error: 2.4750\n",
      "Epoch 252/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.7273 - root_mean_squared_error: 0.8528 - val_loss: 6.0557 - val_root_mean_squared_error: 2.4608\n",
      "Epoch 253/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.7222 - root_mean_squared_error: 0.8498 - val_loss: 6.0070 - val_root_mean_squared_error: 2.4509\n",
      "Epoch 254/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.7095 - root_mean_squared_error: 0.8423 - val_loss: 5.9463 - val_root_mean_squared_error: 2.4385\n",
      "Epoch 255/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.7058 - root_mean_squared_error: 0.8401 - val_loss: 5.8738 - val_root_mean_squared_error: 2.4236\n",
      "Epoch 256/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.6837 - root_mean_squared_error: 0.8268 - val_loss: 5.8223 - val_root_mean_squared_error: 2.4129\n",
      "Epoch 257/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.6732 - root_mean_squared_error: 0.8205 - val_loss: 5.7521 - val_root_mean_squared_error: 2.3984\n",
      "Epoch 258/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.6608 - root_mean_squared_error: 0.8129 - val_loss: 5.6962 - val_root_mean_squared_error: 2.3867\n",
      "Epoch 259/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.6538 - root_mean_squared_error: 0.8086 - val_loss: 5.6275 - val_root_mean_squared_error: 2.3722\n",
      "Epoch 260/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.6429 - root_mean_squared_error: 0.8018 - val_loss: 5.5686 - val_root_mean_squared_error: 2.3598\n",
      "Epoch 261/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.6325 - root_mean_squared_error: 0.7953 - val_loss: 5.5190 - val_root_mean_squared_error: 2.3493\n",
      "Epoch 262/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.6212 - root_mean_squared_error: 0.7882 - val_loss: 5.4742 - val_root_mean_squared_error: 2.3397\n",
      "Epoch 263/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.6134 - root_mean_squared_error: 0.7832 - val_loss: 5.3957 - val_root_mean_squared_error: 2.3229\n",
      "Epoch 264/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.6006 - root_mean_squared_error: 0.7750 - val_loss: 5.3381 - val_root_mean_squared_error: 2.3104\n",
      "Epoch 265/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.5927 - root_mean_squared_error: 0.7699 - val_loss: 5.2834 - val_root_mean_squared_error: 2.2986\n",
      "Epoch 266/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.5827 - root_mean_squared_error: 0.7634 - val_loss: 5.2199 - val_root_mean_squared_error: 2.2847\n",
      "Epoch 267/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.5766 - root_mean_squared_error: 0.7593 - val_loss: 5.1695 - val_root_mean_squared_error: 2.2737\n",
      "Epoch 268/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.5669 - root_mean_squared_error: 0.7529 - val_loss: 5.1047 - val_root_mean_squared_error: 2.2594\n",
      "Epoch 269/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.5615 - root_mean_squared_error: 0.7493 - val_loss: 5.0686 - val_root_mean_squared_error: 2.2514\n",
      "Epoch 270/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.5500 - root_mean_squared_error: 0.7416 - val_loss: 5.0627 - val_root_mean_squared_error: 2.2500\n",
      "Epoch 271/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.5449 - root_mean_squared_error: 0.7382 - val_loss: 4.9664 - val_root_mean_squared_error: 2.2285\n",
      "Epoch 272/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.5324 - root_mean_squared_error: 0.7297 - val_loss: 4.9191 - val_root_mean_squared_error: 2.2179\n",
      "Epoch 273/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.5234 - root_mean_squared_error: 0.7235 - val_loss: 4.8639 - val_root_mean_squared_error: 2.2054\n",
      "Epoch 274/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.5150 - root_mean_squared_error: 0.7177 - val_loss: 4.8245 - val_root_mean_squared_error: 2.1965\n",
      "Epoch 275/450\n",
      "9/9 [==============================] - 0s 5ms/step - loss: 0.5110 - root_mean_squared_error: 0.7148 - val_loss: 4.7576 - val_root_mean_squared_error: 2.1812\n",
      "Epoch 276/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.5023 - root_mean_squared_error: 0.7087 - val_loss: 4.7121 - val_root_mean_squared_error: 2.1707\n",
      "Epoch 277/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.5010 - root_mean_squared_error: 0.7078 - val_loss: 4.6844 - val_root_mean_squared_error: 2.1644\n",
      "Epoch 278/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.4872 - root_mean_squared_error: 0.6980 - val_loss: 4.6382 - val_root_mean_squared_error: 2.1536\n",
      "Epoch 279/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.4775 - root_mean_squared_error: 0.6910 - val_loss: 4.5963 - val_root_mean_squared_error: 2.1439\n",
      "Epoch 280/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.4710 - root_mean_squared_error: 0.6863 - val_loss: 4.5589 - val_root_mean_squared_error: 2.1352\n",
      "Epoch 281/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.4640 - root_mean_squared_error: 0.6812 - val_loss: 4.5043 - val_root_mean_squared_error: 2.1223\n",
      "Epoch 282/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.4543 - root_mean_squared_error: 0.6740 - val_loss: 4.4487 - val_root_mean_squared_error: 2.1092\n",
      "Epoch 283/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.4463 - root_mean_squared_error: 0.6681 - val_loss: 4.4188 - val_root_mean_squared_error: 2.1021\n",
      "Epoch 284/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.4405 - root_mean_squared_error: 0.6637 - val_loss: 4.3819 - val_root_mean_squared_error: 2.0933\n",
      "Epoch 285/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.4373 - root_mean_squared_error: 0.6613 - val_loss: 4.3411 - val_root_mean_squared_error: 2.0835\n",
      "Epoch 286/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.4273 - root_mean_squared_error: 0.6537 - val_loss: 4.2821 - val_root_mean_squared_error: 2.0693\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 287/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.4152 - root_mean_squared_error: 0.6444 - val_loss: 4.2349 - val_root_mean_squared_error: 2.0579\n",
      "Epoch 288/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.4077 - root_mean_squared_error: 0.6385 - val_loss: 4.2056 - val_root_mean_squared_error: 2.0508\n",
      "Epoch 289/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.3979 - root_mean_squared_error: 0.6308 - val_loss: 4.1648 - val_root_mean_squared_error: 2.0408\n",
      "Epoch 290/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.3902 - root_mean_squared_error: 0.6247 - val_loss: 4.1375 - val_root_mean_squared_error: 2.0341\n",
      "Epoch 291/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.3826 - root_mean_squared_error: 0.6186 - val_loss: 4.0813 - val_root_mean_squared_error: 2.0202\n",
      "Epoch 292/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.3748 - root_mean_squared_error: 0.6122 - val_loss: 4.0235 - val_root_mean_squared_error: 2.0059\n",
      "Epoch 293/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.3664 - root_mean_squared_error: 0.6053 - val_loss: 3.9828 - val_root_mean_squared_error: 1.9957\n",
      "Epoch 294/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.3608 - root_mean_squared_error: 0.6007 - val_loss: 3.9407 - val_root_mean_squared_error: 1.9851\n",
      "Epoch 295/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.3537 - root_mean_squared_error: 0.5947 - val_loss: 3.8923 - val_root_mean_squared_error: 1.9729\n",
      "Epoch 296/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.3471 - root_mean_squared_error: 0.5892 - val_loss: 3.8618 - val_root_mean_squared_error: 1.9652\n",
      "Epoch 297/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.3398 - root_mean_squared_error: 0.5829 - val_loss: 3.8167 - val_root_mean_squared_error: 1.9536\n",
      "Epoch 298/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.3356 - root_mean_squared_error: 0.5794 - val_loss: 3.7866 - val_root_mean_squared_error: 1.9459\n",
      "Epoch 299/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.3325 - root_mean_squared_error: 0.5766 - val_loss: 3.7443 - val_root_mean_squared_error: 1.9350\n",
      "Epoch 300/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.3417 - root_mean_squared_error: 0.5845 - val_loss: 3.6924 - val_root_mean_squared_error: 1.9216\n",
      "Epoch 301/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.3299 - root_mean_squared_error: 0.5744 - val_loss: 3.6486 - val_root_mean_squared_error: 1.9101\n",
      "Epoch 302/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.3290 - root_mean_squared_error: 0.5736 - val_loss: 3.6580 - val_root_mean_squared_error: 1.9126\n",
      "Epoch 303/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.3117 - root_mean_squared_error: 0.5583 - val_loss: 3.6155 - val_root_mean_squared_error: 1.9015\n",
      "Epoch 304/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.3056 - root_mean_squared_error: 0.5528 - val_loss: 3.5622 - val_root_mean_squared_error: 1.8874\n",
      "Epoch 305/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.2999 - root_mean_squared_error: 0.5477 - val_loss: 3.5202 - val_root_mean_squared_error: 1.8762\n",
      "Epoch 306/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.2954 - root_mean_squared_error: 0.5435 - val_loss: 3.5062 - val_root_mean_squared_error: 1.8725\n",
      "Epoch 307/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.2938 - root_mean_squared_error: 0.5420 - val_loss: 3.4431 - val_root_mean_squared_error: 1.8556\n",
      "Epoch 308/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.2881 - root_mean_squared_error: 0.5367 - val_loss: 3.4161 - val_root_mean_squared_error: 1.8483\n",
      "Epoch 309/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.2827 - root_mean_squared_error: 0.5317 - val_loss: 3.3884 - val_root_mean_squared_error: 1.8408\n",
      "Epoch 310/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.2793 - root_mean_squared_error: 0.5285 - val_loss: 3.3584 - val_root_mean_squared_error: 1.8326\n",
      "Epoch 311/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.2748 - root_mean_squared_error: 0.5242 - val_loss: 3.3326 - val_root_mean_squared_error: 1.8255\n",
      "Epoch 312/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.2703 - root_mean_squared_error: 0.5199 - val_loss: 3.3006 - val_root_mean_squared_error: 1.8168\n",
      "Epoch 313/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.2666 - root_mean_squared_error: 0.5163 - val_loss: 3.2546 - val_root_mean_squared_error: 1.8041\n",
      "Epoch 314/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.2614 - root_mean_squared_error: 0.5112 - val_loss: 3.2219 - val_root_mean_squared_error: 1.7950\n",
      "Epoch 315/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.2585 - root_mean_squared_error: 0.5085 - val_loss: 3.2010 - val_root_mean_squared_error: 1.7891\n",
      "Epoch 316/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.2541 - root_mean_squared_error: 0.5041 - val_loss: 3.1678 - val_root_mean_squared_error: 1.7798\n",
      "Epoch 317/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.2502 - root_mean_squared_error: 0.5002 - val_loss: 3.1339 - val_root_mean_squared_error: 1.7703\n",
      "Epoch 318/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.2453 - root_mean_squared_error: 0.4953 - val_loss: 3.1002 - val_root_mean_squared_error: 1.7607\n",
      "Epoch 319/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.2419 - root_mean_squared_error: 0.4918 - val_loss: 3.0675 - val_root_mean_squared_error: 1.7514\n",
      "Epoch 320/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.2406 - root_mean_squared_error: 0.4905 - val_loss: 3.0375 - val_root_mean_squared_error: 1.7428\n",
      "Epoch 321/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.2356 - root_mean_squared_error: 0.4853 - val_loss: 3.0100 - val_root_mean_squared_error: 1.7349\n",
      "Epoch 322/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.2319 - root_mean_squared_error: 0.4816 - val_loss: 2.9813 - val_root_mean_squared_error: 1.7267\n",
      "Epoch 323/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.2278 - root_mean_squared_error: 0.4772 - val_loss: 2.9442 - val_root_mean_squared_error: 1.7159\n",
      "Epoch 324/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.2250 - root_mean_squared_error: 0.4743 - val_loss: 2.9121 - val_root_mean_squared_error: 1.7065\n",
      "Epoch 325/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.2322 - root_mean_squared_error: 0.4819 - val_loss: 2.8775 - val_root_mean_squared_error: 1.6963\n",
      "Epoch 326/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.2261 - root_mean_squared_error: 0.4755 - val_loss: 2.8591 - val_root_mean_squared_error: 1.6909\n",
      "Epoch 327/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.2160 - root_mean_squared_error: 0.4648 - val_loss: 2.8382 - val_root_mean_squared_error: 1.6847\n",
      "Epoch 328/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.2112 - root_mean_squared_error: 0.4596 - val_loss: 2.7986 - val_root_mean_squared_error: 1.6729\n",
      "Epoch 329/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.2073 - root_mean_squared_error: 0.4553 - val_loss: 2.7758 - val_root_mean_squared_error: 1.6661\n",
      "Epoch 330/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.2047 - root_mean_squared_error: 0.4525 - val_loss: 2.7582 - val_root_mean_squared_error: 1.6608\n",
      "Epoch 331/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.2011 - root_mean_squared_error: 0.4485 - val_loss: 2.7421 - val_root_mean_squared_error: 1.6559\n",
      "Epoch 332/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.2003 - root_mean_squared_error: 0.4475 - val_loss: 2.7121 - val_root_mean_squared_error: 1.6468\n",
      "Epoch 333/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.1974 - root_mean_squared_error: 0.4443 - val_loss: 2.6829 - val_root_mean_squared_error: 1.6380\n",
      "Epoch 334/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.1999 - root_mean_squared_error: 0.4471 - val_loss: 2.6497 - val_root_mean_squared_error: 1.6278\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 335/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.1946 - root_mean_squared_error: 0.4411 - val_loss: 2.6449 - val_root_mean_squared_error: 1.6263\n",
      "Epoch 336/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.1893 - root_mean_squared_error: 0.4350 - val_loss: 2.6102 - val_root_mean_squared_error: 1.6156\n",
      "Epoch 337/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.1845 - root_mean_squared_error: 0.4295 - val_loss: 2.5763 - val_root_mean_squared_error: 1.6051\n",
      "Epoch 338/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.1837 - root_mean_squared_error: 0.4287 - val_loss: 2.5569 - val_root_mean_squared_error: 1.5990\n",
      "Epoch 339/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.1799 - root_mean_squared_error: 0.4241 - val_loss: 2.5286 - val_root_mean_squared_error: 1.5902\n",
      "Epoch 340/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.1766 - root_mean_squared_error: 0.4203 - val_loss: 2.5098 - val_root_mean_squared_error: 1.5842\n",
      "Epoch 341/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.1743 - root_mean_squared_error: 0.4174 - val_loss: 2.5280 - val_root_mean_squared_error: 1.5900\n",
      "Epoch 342/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.1758 - root_mean_squared_error: 0.4192 - val_loss: 2.4764 - val_root_mean_squared_error: 1.5737\n",
      "Epoch 343/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.1723 - root_mean_squared_error: 0.4151 - val_loss: 2.4282 - val_root_mean_squared_error: 1.5583\n",
      "Epoch 344/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.1723 - root_mean_squared_error: 0.4151 - val_loss: 2.4034 - val_root_mean_squared_error: 1.5503\n",
      "Epoch 345/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.1672 - root_mean_squared_error: 0.4089 - val_loss: 2.3894 - val_root_mean_squared_error: 1.5458\n",
      "Epoch 346/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.1611 - root_mean_squared_error: 0.4014 - val_loss: 2.3540 - val_root_mean_squared_error: 1.5343\n",
      "Epoch 347/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.1572 - root_mean_squared_error: 0.3965 - val_loss: 2.3438 - val_root_mean_squared_error: 1.5309\n",
      "Epoch 348/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.1530 - root_mean_squared_error: 0.3912 - val_loss: 2.3062 - val_root_mean_squared_error: 1.5186\n",
      "Epoch 349/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.1517 - root_mean_squared_error: 0.3895 - val_loss: 2.2796 - val_root_mean_squared_error: 1.5098\n",
      "Epoch 350/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.1518 - root_mean_squared_error: 0.3896 - val_loss: 2.2647 - val_root_mean_squared_error: 1.5049\n",
      "Epoch 351/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.1470 - root_mean_squared_error: 0.3834 - val_loss: 2.2409 - val_root_mean_squared_error: 1.4969\n",
      "Epoch 352/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.1461 - root_mean_squared_error: 0.3822 - val_loss: 2.2333 - val_root_mean_squared_error: 1.4944\n",
      "Epoch 353/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.1445 - root_mean_squared_error: 0.3801 - val_loss: 2.2048 - val_root_mean_squared_error: 1.4849\n",
      "Epoch 354/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.1395 - root_mean_squared_error: 0.3735 - val_loss: 2.1864 - val_root_mean_squared_error: 1.4787\n",
      "Epoch 355/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.1369 - root_mean_squared_error: 0.3700 - val_loss: 2.1642 - val_root_mean_squared_error: 1.4711\n",
      "Epoch 356/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.1359 - root_mean_squared_error: 0.3686 - val_loss: 2.1507 - val_root_mean_squared_error: 1.4665\n",
      "Epoch 357/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.1327 - root_mean_squared_error: 0.3643 - val_loss: 2.1132 - val_root_mean_squared_error: 1.4537\n",
      "Epoch 358/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.1397 - root_mean_squared_error: 0.3737 - val_loss: 2.0911 - val_root_mean_squared_error: 1.4461\n",
      "Epoch 359/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.1445 - root_mean_squared_error: 0.3802 - val_loss: 2.0705 - val_root_mean_squared_error: 1.4389\n",
      "Epoch 360/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.1323 - root_mean_squared_error: 0.3637 - val_loss: 2.1015 - val_root_mean_squared_error: 1.4497\n",
      "Epoch 361/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.1316 - root_mean_squared_error: 0.3628 - val_loss: 2.0504 - val_root_mean_squared_error: 1.4319\n",
      "Epoch 362/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.1245 - root_mean_squared_error: 0.3529 - val_loss: 2.0500 - val_root_mean_squared_error: 1.4318\n",
      "Epoch 363/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.1253 - root_mean_squared_error: 0.3539 - val_loss: 2.0147 - val_root_mean_squared_error: 1.4194\n",
      "Epoch 364/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.1219 - root_mean_squared_error: 0.3491 - val_loss: 1.9989 - val_root_mean_squared_error: 1.4138\n",
      "Epoch 365/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.1206 - root_mean_squared_error: 0.3472 - val_loss: 1.9628 - val_root_mean_squared_error: 1.4010\n",
      "Epoch 366/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.1221 - root_mean_squared_error: 0.3495 - val_loss: 1.9458 - val_root_mean_squared_error: 1.3949\n",
      "Epoch 367/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.1174 - root_mean_squared_error: 0.3427 - val_loss: 1.9344 - val_root_mean_squared_error: 1.3908\n",
      "Epoch 368/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.1138 - root_mean_squared_error: 0.3374 - val_loss: 1.9221 - val_root_mean_squared_error: 1.3864\n",
      "Epoch 369/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.1134 - root_mean_squared_error: 0.3368 - val_loss: 1.9094 - val_root_mean_squared_error: 1.3818\n",
      "Epoch 370/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.1116 - root_mean_squared_error: 0.3340 - val_loss: 1.8944 - val_root_mean_squared_error: 1.3764\n",
      "Epoch 371/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.1125 - root_mean_squared_error: 0.3354 - val_loss: 1.8708 - val_root_mean_squared_error: 1.3678\n",
      "Epoch 372/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.1169 - root_mean_squared_error: 0.3419 - val_loss: 1.8402 - val_root_mean_squared_error: 1.3565\n",
      "Epoch 373/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.1076 - root_mean_squared_error: 0.3280 - val_loss: 1.8429 - val_root_mean_squared_error: 1.3575\n",
      "Epoch 374/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.1070 - root_mean_squared_error: 0.3272 - val_loss: 1.8207 - val_root_mean_squared_error: 1.3493\n",
      "Epoch 375/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.1030 - root_mean_squared_error: 0.3210 - val_loss: 1.7930 - val_root_mean_squared_error: 1.3390\n",
      "Epoch 376/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0993 - root_mean_squared_error: 0.3151 - val_loss: 1.7961 - val_root_mean_squared_error: 1.3402\n",
      "Epoch 377/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.1016 - root_mean_squared_error: 0.3187 - val_loss: 1.7950 - val_root_mean_squared_error: 1.3398\n",
      "Epoch 378/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.1016 - root_mean_squared_error: 0.3188 - val_loss: 1.7541 - val_root_mean_squared_error: 1.3244\n",
      "Epoch 379/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.1008 - root_mean_squared_error: 0.3175 - val_loss: 1.7378 - val_root_mean_squared_error: 1.3183\n",
      "Epoch 380/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0939 - root_mean_squared_error: 0.3064 - val_loss: 1.7215 - val_root_mean_squared_error: 1.3121\n",
      "Epoch 381/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0932 - root_mean_squared_error: 0.3052 - val_loss: 1.7003 - val_root_mean_squared_error: 1.3039\n",
      "Epoch 382/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0910 - root_mean_squared_error: 0.3016 - val_loss: 1.6760 - val_root_mean_squared_error: 1.2946\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 383/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0896 - root_mean_squared_error: 0.2993 - val_loss: 1.6734 - val_root_mean_squared_error: 1.2936\n",
      "Epoch 384/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0878 - root_mean_squared_error: 0.2963 - val_loss: 1.6567 - val_root_mean_squared_error: 1.2871\n",
      "Epoch 385/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0860 - root_mean_squared_error: 0.2933 - val_loss: 1.6337 - val_root_mean_squared_error: 1.2782\n",
      "Epoch 386/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0885 - root_mean_squared_error: 0.2975 - val_loss: 1.6135 - val_root_mean_squared_error: 1.2703\n",
      "Epoch 387/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0876 - root_mean_squared_error: 0.2959 - val_loss: 1.6183 - val_root_mean_squared_error: 1.2721\n",
      "Epoch 388/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0854 - root_mean_squared_error: 0.2921 - val_loss: 1.5886 - val_root_mean_squared_error: 1.2604\n",
      "Epoch 389/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0824 - root_mean_squared_error: 0.2870 - val_loss: 1.5735 - val_root_mean_squared_error: 1.2544\n",
      "Epoch 390/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0841 - root_mean_squared_error: 0.2900 - val_loss: 1.5635 - val_root_mean_squared_error: 1.2504\n",
      "Epoch 391/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0797 - root_mean_squared_error: 0.2823 - val_loss: 1.5765 - val_root_mean_squared_error: 1.2556\n",
      "Epoch 392/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0833 - root_mean_squared_error: 0.2886 - val_loss: 1.5420 - val_root_mean_squared_error: 1.2418\n",
      "Epoch 393/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0772 - root_mean_squared_error: 0.2779 - val_loss: 1.5350 - val_root_mean_squared_error: 1.2390\n",
      "Epoch 394/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0761 - root_mean_squared_error: 0.2759 - val_loss: 1.5181 - val_root_mean_squared_error: 1.2321\n",
      "Epoch 395/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0742 - root_mean_squared_error: 0.2724 - val_loss: 1.4987 - val_root_mean_squared_error: 1.2242\n",
      "Epoch 396/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0736 - root_mean_squared_error: 0.2713 - val_loss: 1.4984 - val_root_mean_squared_error: 1.2241\n",
      "Epoch 397/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0755 - root_mean_squared_error: 0.2748 - val_loss: 1.5105 - val_root_mean_squared_error: 1.2290\n",
      "Epoch 398/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0768 - root_mean_squared_error: 0.2772 - val_loss: 1.4676 - val_root_mean_squared_error: 1.2115\n",
      "Epoch 399/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0755 - root_mean_squared_error: 0.2748 - val_loss: 1.4466 - val_root_mean_squared_error: 1.2028\n",
      "Epoch 400/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0713 - root_mean_squared_error: 0.2671 - val_loss: 1.4306 - val_root_mean_squared_error: 1.1961\n",
      "Epoch 401/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0692 - root_mean_squared_error: 0.2632 - val_loss: 1.4382 - val_root_mean_squared_error: 1.1992\n",
      "Epoch 402/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0738 - root_mean_squared_error: 0.2717 - val_loss: 1.4369 - val_root_mean_squared_error: 1.1987\n",
      "Epoch 403/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0691 - root_mean_squared_error: 0.2628 - val_loss: 1.4008 - val_root_mean_squared_error: 1.1835\n",
      "Epoch 404/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0655 - root_mean_squared_error: 0.2559 - val_loss: 1.3922 - val_root_mean_squared_error: 1.1799\n",
      "Epoch 405/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0649 - root_mean_squared_error: 0.2547 - val_loss: 1.3811 - val_root_mean_squared_error: 1.1752\n",
      "Epoch 406/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0639 - root_mean_squared_error: 0.2529 - val_loss: 1.3675 - val_root_mean_squared_error: 1.1694\n",
      "Epoch 407/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0621 - root_mean_squared_error: 0.2493 - val_loss: 1.3638 - val_root_mean_squared_error: 1.1678\n",
      "Epoch 408/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0622 - root_mean_squared_error: 0.2495 - val_loss: 1.3446 - val_root_mean_squared_error: 1.1596\n",
      "Epoch 409/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0630 - root_mean_squared_error: 0.2510 - val_loss: 1.3248 - val_root_mean_squared_error: 1.1510\n",
      "Epoch 410/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0612 - root_mean_squared_error: 0.2473 - val_loss: 1.3219 - val_root_mean_squared_error: 1.1497\n",
      "Epoch 411/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0624 - root_mean_squared_error: 0.2497 - val_loss: 1.3127 - val_root_mean_squared_error: 1.1457\n",
      "Epoch 412/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0579 - root_mean_squared_error: 0.2405 - val_loss: 1.3032 - val_root_mean_squared_error: 1.1416\n",
      "Epoch 413/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0599 - root_mean_squared_error: 0.2447 - val_loss: 1.3126 - val_root_mean_squared_error: 1.1457\n",
      "Epoch 414/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0594 - root_mean_squared_error: 0.2437 - val_loss: 1.2643 - val_root_mean_squared_error: 1.1244\n",
      "Epoch 415/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0576 - root_mean_squared_error: 0.2401 - val_loss: 1.2773 - val_root_mean_squared_error: 1.1302\n",
      "Epoch 416/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0563 - root_mean_squared_error: 0.2373 - val_loss: 1.2410 - val_root_mean_squared_error: 1.1140\n",
      "Epoch 417/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0555 - root_mean_squared_error: 0.2356 - val_loss: 1.2372 - val_root_mean_squared_error: 1.1123\n",
      "Epoch 418/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0543 - root_mean_squared_error: 0.2330 - val_loss: 1.2222 - val_root_mean_squared_error: 1.1055\n",
      "Epoch 419/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0552 - root_mean_squared_error: 0.2349 - val_loss: 1.2072 - val_root_mean_squared_error: 1.0987\n",
      "Epoch 420/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0584 - root_mean_squared_error: 0.2417 - val_loss: 1.2066 - val_root_mean_squared_error: 1.0985\n",
      "Epoch 421/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0562 - root_mean_squared_error: 0.2371 - val_loss: 1.2098 - val_root_mean_squared_error: 1.0999\n",
      "Epoch 422/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0593 - root_mean_squared_error: 0.2435 - val_loss: 1.2204 - val_root_mean_squared_error: 1.1047\n",
      "Epoch 423/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0539 - root_mean_squared_error: 0.2322 - val_loss: 1.1686 - val_root_mean_squared_error: 1.0810\n",
      "Epoch 424/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0513 - root_mean_squared_error: 0.2266 - val_loss: 1.2071 - val_root_mean_squared_error: 1.0987\n",
      "Epoch 425/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0564 - root_mean_squared_error: 0.2375 - val_loss: 1.1614 - val_root_mean_squared_error: 1.0777\n",
      "Epoch 426/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0487 - root_mean_squared_error: 0.2207 - val_loss: 1.1583 - val_root_mean_squared_error: 1.0762\n",
      "Epoch 427/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0507 - root_mean_squared_error: 0.2251 - val_loss: 1.1484 - val_root_mean_squared_error: 1.0716\n",
      "Epoch 428/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0474 - root_mean_squared_error: 0.2176 - val_loss: 1.1239 - val_root_mean_squared_error: 1.0601\n",
      "Epoch 429/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0487 - root_mean_squared_error: 0.2206 - val_loss: 1.1161 - val_root_mean_squared_error: 1.0565\n",
      "Epoch 430/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0503 - root_mean_squared_error: 0.2242 - val_loss: 1.1038 - val_root_mean_squared_error: 1.0506\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 431/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0473 - root_mean_squared_error: 0.2174 - val_loss: 1.1035 - val_root_mean_squared_error: 1.0505\n",
      "Epoch 432/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0457 - root_mean_squared_error: 0.2138 - val_loss: 1.1002 - val_root_mean_squared_error: 1.0489\n",
      "Epoch 433/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0441 - root_mean_squared_error: 0.2100 - val_loss: 1.0895 - val_root_mean_squared_error: 1.0438\n",
      "Epoch 434/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0435 - root_mean_squared_error: 0.2087 - val_loss: 1.0788 - val_root_mean_squared_error: 1.0386\n",
      "Epoch 435/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0419 - root_mean_squared_error: 0.2047 - val_loss: 1.0647 - val_root_mean_squared_error: 1.0318\n",
      "Epoch 436/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0418 - root_mean_squared_error: 0.2045 - val_loss: 1.0592 - val_root_mean_squared_error: 1.0292\n",
      "Epoch 437/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0416 - root_mean_squared_error: 0.2038 - val_loss: 1.0502 - val_root_mean_squared_error: 1.0248\n",
      "Epoch 438/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0419 - root_mean_squared_error: 0.2046 - val_loss: 1.0524 - val_root_mean_squared_error: 1.0259\n",
      "Epoch 439/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0419 - root_mean_squared_error: 0.2046 - val_loss: 1.0264 - val_root_mean_squared_error: 1.0131\n",
      "Epoch 440/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0403 - root_mean_squared_error: 0.2009 - val_loss: 1.0172 - val_root_mean_squared_error: 1.0086\n",
      "Epoch 441/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0407 - root_mean_squared_error: 0.2017 - val_loss: 1.0156 - val_root_mean_squared_error: 1.0078\n",
      "Epoch 442/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0410 - root_mean_squared_error: 0.2025 - val_loss: 1.0483 - val_root_mean_squared_error: 1.0238\n",
      "Epoch 443/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0495 - root_mean_squared_error: 0.2225 - val_loss: 0.9990 - val_root_mean_squared_error: 0.9995\n",
      "Epoch 444/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0455 - root_mean_squared_error: 0.2132 - val_loss: 0.9842 - val_root_mean_squared_error: 0.9921\n",
      "Epoch 445/450\n",
      "9/9 [==============================] - 0s 5ms/step - loss: 0.0429 - root_mean_squared_error: 0.2072 - val_loss: 0.9738 - val_root_mean_squared_error: 0.9868\n",
      "Epoch 446/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0408 - root_mean_squared_error: 0.2020 - val_loss: 0.9884 - val_root_mean_squared_error: 0.9942\n",
      "Epoch 447/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0432 - root_mean_squared_error: 0.2078 - val_loss: 0.9831 - val_root_mean_squared_error: 0.9915\n",
      "Epoch 448/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0376 - root_mean_squared_error: 0.1939 - val_loss: 0.9658 - val_root_mean_squared_error: 0.9827\n",
      "Epoch 449/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0385 - root_mean_squared_error: 0.1963 - val_loss: 0.9886 - val_root_mean_squared_error: 0.9943\n",
      "Epoch 450/450\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.0388 - root_mean_squared_error: 0.1970 - val_loss: 0.9523 - val_root_mean_squared_error: 0.9759\n"
     ]
    }
   ],
   "source": [
    "# GRU\n",
    "model_gru = Sequential()\n",
    "model_gru.add(InputLayer((7, 8)))\n",
    "model_gru.add(GRU(64, activation='relu', input_shape=(X2_train.shape[1], X2_train.shape[2]),return_sequences=True))\n",
    "model_gru.add(GRU(32))\n",
    "model_gru.add(Dense(8, 'relu'))\n",
    "model_gru.add(Dense(1, 'linear'))\n",
    "# model_gru.summary()\n",
    "model_gru.compile(loss=MeanSquaredError(), optimizer=Adam(learning_rate=0.0001), metrics=[RootMeanSquaredError()])\n",
    "history = model_gru.fit(X2_train, y2_train, batch_size=32, validation_data=(X2_val, y2_val), epochs=450)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1400,
   "id": "ea58e989",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/80\n",
      "9/9 [==============================] - 1s 13ms/step - loss: 26.0527 - root_mean_squared_error: 5.1042 - val_loss: 8.6549 - val_root_mean_squared_error: 2.9419\n",
      "Epoch 2/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 25.1312 - root_mean_squared_error: 5.0131 - val_loss: 8.3175 - val_root_mean_squared_error: 2.8840\n",
      "Epoch 3/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 24.2049 - root_mean_squared_error: 4.9198 - val_loss: 7.9731 - val_root_mean_squared_error: 2.8237\n",
      "Epoch 4/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 23.2781 - root_mean_squared_error: 4.8247 - val_loss: 7.6244 - val_root_mean_squared_error: 2.7612\n",
      "Epoch 5/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 22.3392 - root_mean_squared_error: 4.7264 - val_loss: 7.2757 - val_root_mean_squared_error: 2.6973\n",
      "Epoch 6/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 21.3967 - root_mean_squared_error: 4.6257 - val_loss: 6.9130 - val_root_mean_squared_error: 2.6293\n",
      "Epoch 7/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 20.4444 - root_mean_squared_error: 4.5215 - val_loss: 6.5512 - val_root_mean_squared_error: 2.5595\n",
      "Epoch 8/80\n",
      "9/9 [==============================] - 0s 5ms/step - loss: 19.4648 - root_mean_squared_error: 4.4119 - val_loss: 6.1872 - val_root_mean_squared_error: 2.4874\n",
      "Epoch 9/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 18.4773 - root_mean_squared_error: 4.2985 - val_loss: 5.8276 - val_root_mean_squared_error: 2.4140\n",
      "Epoch 10/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 17.4676 - root_mean_squared_error: 4.1794 - val_loss: 5.4664 - val_root_mean_squared_error: 2.3380\n",
      "Epoch 11/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 16.4542 - root_mean_squared_error: 4.0564 - val_loss: 5.0931 - val_root_mean_squared_error: 2.2568\n",
      "Epoch 12/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 15.4303 - root_mean_squared_error: 3.9281 - val_loss: 4.7081 - val_root_mean_squared_error: 2.1698\n",
      "Epoch 13/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 14.4054 - root_mean_squared_error: 3.7954 - val_loss: 4.3044 - val_root_mean_squared_error: 2.0747\n",
      "Epoch 14/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 13.3660 - root_mean_squared_error: 3.6560 - val_loss: 3.8983 - val_root_mean_squared_error: 1.9744\n",
      "Epoch 15/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 12.3148 - root_mean_squared_error: 3.5092 - val_loss: 3.4796 - val_root_mean_squared_error: 1.8654\n",
      "Epoch 16/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 11.2372 - root_mean_squared_error: 3.3522 - val_loss: 3.0750 - val_root_mean_squared_error: 1.7536\n",
      "Epoch 17/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 10.1492 - root_mean_squared_error: 3.1858 - val_loss: 2.6853 - val_root_mean_squared_error: 1.6387\n",
      "Epoch 18/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 9.0415 - root_mean_squared_error: 3.0069 - val_loss: 2.3033 - val_root_mean_squared_error: 1.5177\n",
      "Epoch 19/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 7.9087 - root_mean_squared_error: 2.8122 - val_loss: 1.9244 - val_root_mean_squared_error: 1.3872\n",
      "Epoch 20/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 6.7551 - root_mean_squared_error: 2.5991 - val_loss: 1.5571 - val_root_mean_squared_error: 1.2478\n",
      "Epoch 21/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 5.5904 - root_mean_squared_error: 2.3644 - val_loss: 1.2115 - val_root_mean_squared_error: 1.1007\n",
      "Epoch 22/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 4.4323 - root_mean_squared_error: 2.1053 - val_loss: 0.8981 - val_root_mean_squared_error: 0.9477\n",
      "Epoch 23/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 3.3523 - root_mean_squared_error: 1.8309 - val_loss: 0.6252 - val_root_mean_squared_error: 0.7907\n",
      "Epoch 24/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 2.3688 - root_mean_squared_error: 1.5391 - val_loss: 0.4093 - val_root_mean_squared_error: 0.6398\n",
      "Epoch 25/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 1.5474 - root_mean_squared_error: 1.2439 - val_loss: 0.2553 - val_root_mean_squared_error: 0.5053\n",
      "Epoch 26/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.9108 - root_mean_squared_error: 0.9544 - val_loss: 0.1650 - val_root_mean_squared_error: 0.4062\n",
      "Epoch 27/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.4885 - root_mean_squared_error: 0.6989 - val_loss: 0.1282 - val_root_mean_squared_error: 0.3581\n",
      "Epoch 28/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.2415 - root_mean_squared_error: 0.4914 - val_loss: 0.1290 - val_root_mean_squared_error: 0.3591\n",
      "Epoch 29/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1431 - root_mean_squared_error: 0.3783 - val_loss: 0.1455 - val_root_mean_squared_error: 0.3814\n",
      "Epoch 30/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1105 - root_mean_squared_error: 0.3324 - val_loss: 0.1576 - val_root_mean_squared_error: 0.3970\n",
      "Epoch 31/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1064 - root_mean_squared_error: 0.3261 - val_loss: 0.1615 - val_root_mean_squared_error: 0.4019\n",
      "Epoch 32/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1032 - root_mean_squared_error: 0.3212 - val_loss: 0.1572 - val_root_mean_squared_error: 0.3965\n",
      "Epoch 33/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0993 - root_mean_squared_error: 0.3151 - val_loss: 0.1521 - val_root_mean_squared_error: 0.3900\n",
      "Epoch 34/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0959 - root_mean_squared_error: 0.3098 - val_loss: 0.1487 - val_root_mean_squared_error: 0.3856\n",
      "Epoch 35/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0933 - root_mean_squared_error: 0.3054 - val_loss: 0.1447 - val_root_mean_squared_error: 0.3805\n",
      "Epoch 36/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0907 - root_mean_squared_error: 0.3011 - val_loss: 0.1436 - val_root_mean_squared_error: 0.3789\n",
      "Epoch 37/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0882 - root_mean_squared_error: 0.2969 - val_loss: 0.1413 - val_root_mean_squared_error: 0.3759\n",
      "Epoch 38/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0857 - root_mean_squared_error: 0.2927 - val_loss: 0.1393 - val_root_mean_squared_error: 0.3732\n",
      "Epoch 39/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0835 - root_mean_squared_error: 0.2890 - val_loss: 0.1364 - val_root_mean_squared_error: 0.3694\n",
      "Epoch 40/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0810 - root_mean_squared_error: 0.2846 - val_loss: 0.1355 - val_root_mean_squared_error: 0.3681\n",
      "Epoch 41/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0785 - root_mean_squared_error: 0.2801 - val_loss: 0.1349 - val_root_mean_squared_error: 0.3673\n",
      "Epoch 42/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0761 - root_mean_squared_error: 0.2759 - val_loss: 0.1344 - val_root_mean_squared_error: 0.3666\n",
      "Epoch 43/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0744 - root_mean_squared_error: 0.2728 - val_loss: 0.1339 - val_root_mean_squared_error: 0.3659\n",
      "Epoch 44/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0725 - root_mean_squared_error: 0.2692 - val_loss: 0.1315 - val_root_mean_squared_error: 0.3626\n",
      "Epoch 45/80\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0705 - root_mean_squared_error: 0.2656 - val_loss: 0.1299 - val_root_mean_squared_error: 0.3604\n",
      "Epoch 46/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0686 - root_mean_squared_error: 0.2620 - val_loss: 0.1282 - val_root_mean_squared_error: 0.3580\n",
      "Epoch 47/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0670 - root_mean_squared_error: 0.2588 - val_loss: 0.1262 - val_root_mean_squared_error: 0.3553\n",
      "Epoch 48/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0651 - root_mean_squared_error: 0.2551 - val_loss: 0.1239 - val_root_mean_squared_error: 0.3519\n",
      "Epoch 49/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0631 - root_mean_squared_error: 0.2513 - val_loss: 0.1214 - val_root_mean_squared_error: 0.3484\n",
      "Epoch 50/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0613 - root_mean_squared_error: 0.2475 - val_loss: 0.1186 - val_root_mean_squared_error: 0.3443\n",
      "Epoch 51/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0596 - root_mean_squared_error: 0.2441 - val_loss: 0.1164 - val_root_mean_squared_error: 0.3412\n",
      "Epoch 52/80\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0579 - root_mean_squared_error: 0.2405 - val_loss: 0.1169 - val_root_mean_squared_error: 0.3418\n",
      "Epoch 53/80\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0560 - root_mean_squared_error: 0.2367 - val_loss: 0.1165 - val_root_mean_squared_error: 0.3414\n",
      "Epoch 54/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0547 - root_mean_squared_error: 0.2338 - val_loss: 0.1166 - val_root_mean_squared_error: 0.3414\n",
      "Epoch 55/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0534 - root_mean_squared_error: 0.2310 - val_loss: 0.1158 - val_root_mean_squared_error: 0.3403\n",
      "Epoch 56/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0518 - root_mean_squared_error: 0.2277 - val_loss: 0.1128 - val_root_mean_squared_error: 0.3358\n",
      "Epoch 57/80\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0504 - root_mean_squared_error: 0.2246 - val_loss: 0.1114 - val_root_mean_squared_error: 0.3338\n",
      "Epoch 58/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0490 - root_mean_squared_error: 0.2214 - val_loss: 0.1102 - val_root_mean_squared_error: 0.3320\n",
      "Epoch 59/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0477 - root_mean_squared_error: 0.2184 - val_loss: 0.1088 - val_root_mean_squared_error: 0.3299\n",
      "Epoch 60/80\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0463 - root_mean_squared_error: 0.2152 - val_loss: 0.1088 - val_root_mean_squared_error: 0.3298\n",
      "Epoch 61/80\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0453 - root_mean_squared_error: 0.2129 - val_loss: 0.1086 - val_root_mean_squared_error: 0.3295\n",
      "Epoch 62/80\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0442 - root_mean_squared_error: 0.2103 - val_loss: 0.1073 - val_root_mean_squared_error: 0.3276\n",
      "Epoch 63/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0432 - root_mean_squared_error: 0.2077 - val_loss: 0.1057 - val_root_mean_squared_error: 0.3251\n",
      "Epoch 64/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0422 - root_mean_squared_error: 0.2054 - val_loss: 0.1050 - val_root_mean_squared_error: 0.3240\n",
      "Epoch 65/80\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0410 - root_mean_squared_error: 0.2024 - val_loss: 0.1026 - val_root_mean_squared_error: 0.3203\n",
      "Epoch 66/80\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0398 - root_mean_squared_error: 0.1995 - val_loss: 0.1004 - val_root_mean_squared_error: 0.3169\n",
      "Epoch 67/80\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0389 - root_mean_squared_error: 0.1973 - val_loss: 0.0992 - val_root_mean_squared_error: 0.3149\n",
      "Epoch 68/80\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0380 - root_mean_squared_error: 0.1949 - val_loss: 0.0998 - val_root_mean_squared_error: 0.3159\n",
      "Epoch 69/80\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0369 - root_mean_squared_error: 0.1922 - val_loss: 0.0998 - val_root_mean_squared_error: 0.3160\n",
      "Epoch 70/80\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0360 - root_mean_squared_error: 0.1899 - val_loss: 0.0998 - val_root_mean_squared_error: 0.3159\n",
      "Epoch 71/80\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0352 - root_mean_squared_error: 0.1877 - val_loss: 0.0998 - val_root_mean_squared_error: 0.3158\n",
      "Epoch 72/80\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0345 - root_mean_squared_error: 0.1857 - val_loss: 0.0991 - val_root_mean_squared_error: 0.3147\n",
      "Epoch 73/80\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0337 - root_mean_squared_error: 0.1836 - val_loss: 0.0979 - val_root_mean_squared_error: 0.3128\n",
      "Epoch 74/80\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0331 - root_mean_squared_error: 0.1818 - val_loss: 0.0966 - val_root_mean_squared_error: 0.3108\n",
      "Epoch 75/80\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0324 - root_mean_squared_error: 0.1801 - val_loss: 0.0960 - val_root_mean_squared_error: 0.3099\n",
      "Epoch 76/80\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0318 - root_mean_squared_error: 0.1783 - val_loss: 0.0957 - val_root_mean_squared_error: 0.3094\n",
      "Epoch 77/80\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0311 - root_mean_squared_error: 0.1762 - val_loss: 0.0960 - val_root_mean_squared_error: 0.3098\n",
      "Epoch 78/80\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0304 - root_mean_squared_error: 0.1744 - val_loss: 0.0952 - val_root_mean_squared_error: 0.3085\n",
      "Epoch 79/80\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0298 - root_mean_squared_error: 0.1727 - val_loss: 0.0952 - val_root_mean_squared_error: 0.3085\n",
      "Epoch 80/80\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0292 - root_mean_squared_error: 0.1709 - val_loss: 0.0945 - val_root_mean_squared_error: 0.3074\n"
     ]
    }
   ],
   "source": [
    "# GRU before\n",
    "model_before_gru = Sequential()\n",
    "model_before_gru.add(InputLayer((7, 8)))\n",
    "model_before_gru.add(GRU(32, activation='relu', input_shape=(X2_before_train.shape[1], X2_before_train.shape[2]),return_sequences=False))\n",
    "# model_before_gru.add(GRU(32))\n",
    "# model_before_gru.add(Dense(8, 'relu'))\n",
    "model_before_gru.add(Dense(1, 'linear'))\n",
    "# model_gru.summary()\n",
    "model_before_gru.compile(loss=MeanSquaredError(), optimizer=Adam(learning_rate=0.0001), metrics=[RootMeanSquaredError()])\n",
    "history = model_before_gru.fit(X2_before_train, y2_before_train, batch_size=32, validation_data=(X2_before_val, y2_before_val), epochs=80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4e35c9e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x1600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot train and validation loss\n",
    "plt.figure(figsize=(8,8),dpi=200)\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model train vs validation loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train','validation'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1615,
   "id": "543fbff4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[ 9.37107  ],\n",
       "        [ 9.424944 ],\n",
       "        [ 9.482872 ],\n",
       "        [ 9.484871 ],\n",
       "        [ 9.341769 ],\n",
       "        [ 9.496186 ],\n",
       "        [ 9.663953 ],\n",
       "        [ 9.253588 ],\n",
       "        [ 8.963553 ],\n",
       "        [ 9.0407   ],\n",
       "        [ 9.306767 ],\n",
       "        [ 9.658187 ],\n",
       "        [ 9.740075 ],\n",
       "        [ 9.766276 ],\n",
       "        [ 9.744592 ],\n",
       "        [ 9.731538 ],\n",
       "        [ 9.674319 ],\n",
       "        [ 9.873378 ],\n",
       "        [ 9.928107 ],\n",
       "        [ 9.981231 ],\n",
       "        [ 9.922837 ],\n",
       "        [ 9.938462 ],\n",
       "        [ 9.919463 ],\n",
       "        [ 9.557191 ],\n",
       "        [ 9.553646 ],\n",
       "        [ 9.838422 ],\n",
       "        [ 9.816105 ],\n",
       "        [ 9.9172945],\n",
       "        [10.0840845],\n",
       "        [10.012947 ]], dtype=float32),\n",
       " 55.14135553765472,\n",
       " 7.421650956199431,\n",
       " 3.4310325865281874)"
      ]
     },
     "execution_count": 1615,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用相同阶段预测同一阶段数据\n",
    "y_pred= plot_predictions1(model=model_gru, X=X2_before_test, y=y2_before_test)\n",
    "y_pred\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1535,
   "id": "8049f92f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[-5.9432945],\n",
       "        [-5.9306045],\n",
       "        [-5.7042913],\n",
       "        [-5.4850316],\n",
       "        [-5.236815 ],\n",
       "        [-5.1675076],\n",
       "        [-4.8921285],\n",
       "        [-4.8967037],\n",
       "        [-4.669684 ],\n",
       "        [-4.6476607],\n",
       "        [-4.7636642],\n",
       "        [-4.769876 ],\n",
       "        [-4.601193 ],\n",
       "        [-5.0150094],\n",
       "        [-4.814453 ],\n",
       "        [-4.703036 ],\n",
       "        [-4.3843527],\n",
       "        [-4.3986645],\n",
       "        [-4.36461  ],\n",
       "        [-4.271769 ],\n",
       "        [-4.050422 ],\n",
       "        [-4.2545714],\n",
       "        [-4.3512297],\n",
       "        [-4.3692455],\n",
       "        [-4.1176105],\n",
       "        [-3.8714333],\n",
       "        [-3.4440782],\n",
       "        [-3.3871698],\n",
       "        [-3.7714882],\n",
       "        [-4.021022 ]], dtype=float32),\n",
       " 0.33395879124683847,\n",
       " 0.4799192028988246,\n",
       " 0.09922395441804484)"
      ]
     },
     "execution_count": 1535,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用之前的模型预测后期的数据\n",
    "y_pred= plot_predictions1(model=model_before_lstm, X=X2_before_test, y=y2_before_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1616,
   "id": "0b851058",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30, 8)"
      ]
     },
     "execution_count": 1616,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecast_copies = np.repeat(y_pred[0], 8, axis=-1)\n",
    "forecast_copies.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1617,
   "id": "a445cd3b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "54117820.0\n",
      "54396456.0\n",
      "54696056.0\n",
      "54706396.0\n",
      "53966280.0\n",
      "54764920.0\n",
      "55632600.0\n",
      "53510210.0\n",
      "52010164.0\n",
      "52409160.0\n",
      "53785252.0\n",
      "55602780.0\n",
      "56026300.0\n",
      "56161812.0\n",
      "56049660.0\n",
      "55982148.0\n",
      "55686216.0\n",
      "56715736.0\n",
      "56998796.0\n",
      "57273548.0\n",
      "56971540.0\n",
      "57052350.0\n",
      "56954090.0\n",
      "55080430.0\n",
      "55062100.0\n",
      "56534948.0\n",
      "56419524.0\n",
      "56942870.0\n",
      "57805504.0\n",
      "57437584.0\n"
     ]
    }
   ],
   "source": [
    "y_pred_real = scaler.inverse_transform(forecast_copies)[:, 0]\n",
    "for i in y_pred_real:\n",
    "    print(i)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f17d1818",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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